-------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/christopherblair/Desktop/JOP Replication/jop_blair.log
  log type:  text
 opened on:   3 May 2023, 17:49:21

. 
. ********************************************************************************
. *                                                                FULL ANALYSIS                                                     
>         *
. ********************************************************************************
. 
. clear all

. 
. ssc install reghdfe, replace
checking reghdfe consistency and verifying not already installed...
all files already exist and are up to date.

. ssc install ftools, replace
checking ftools consistency and verifying not already installed...
all files already exist and are up to date.

. ssc install carryforward, replace
checking carryforward consistency and verifying not already installed...
all files already exist and are up to date.

. ssc install grstyle, replace
checking grstyle consistency and verifying not already installed...
all files already exist and are up to date.

. ssc install outreg2, replace
checking outreg2 consistency and verifying not already installed...
all files already exist and are up to date.

. ssc install coefplot, replace
checking coefplot consistency and verifying not already installed...
all files already exist and are up to date.

. ssc install ppmlhdfe, replace
checking ppmlhdfe consistency and verifying not already installed...
all files already exist and are up to date.

. ssc install cmp, replace
checking cmp consistency and verifying not already installed...
all files already exist and are up to date.

. ssc install ghk2, replace
checking ghk2 consistency and verifying not already installed...
all files already exist and are up to date.

. ssc install boottest, replace
checking boottest consistency and verifying not already installed...
all files already exist and are up to date.

. 
. set more off

. set scheme plotplainblind

. macro drop _all

. est drop _all

. set matsize 800

. set seed 8675309

. 
. ** Set Working Directory
. 
. if c(username) == "christopherblair"{
. global dir "~/Desktop/JOP Replication"
. global raw "${dir}/Raw Files"
. global code "${dir}/Code"                                                                               
. }

. 
. cd "${dir}"
/Users/christopherblair/Desktop/JOP Replication

. 
. else if c(username) == "youruser"{
. global dir "~/Desktop/JOP Replication"
. }

. 
. cd "${dir}"
/Users/christopherblair/Desktop/JOP Replication

. 
. ********************************************************************************
. 
. do "${code}/CW Clean and Process.do"

. ********************************************************************************
. *                                                        PROCESS AND CLEAN DATA                                                    
> *
. ********************************************************************************
. 
. clear all

. set more off

. set scheme plotplainblind

. macro drop _all

. est drop _all

. set matsize 800

. set seed 8675309

. 
. ** Set Working Directory
. 
. if c(username) == "christopherblair"{
. global dir "~/Desktop/JOP Replication"
. global raw "${dir}/Raw Files"
. global code "${dir}/Code"                                                                       
. }

. 
. else if c(username) == "youruser"{
. global dir "~/Desktop/JOP Replication"
. global raw "${dir}/Raw Files"
. global code "${dir}/Code"       
. }

. 
. cd "${raw}"
/Users/christopherblair/Desktop/JOP Replication/Raw Files

. 
. ********************************************************************************
. 
. ** Load from Dictionary
. 
. infile using "ams175.dct", clear

dictionary using ams175.dat {
_column(1) int cardnum %1f "Card #"
_column(2) int surveynum %3f "Survey #"
_column(5) int ballotnum %4f "Ballot #"
_column(8) int rankgrade %1f "What is your army rank or grade"
_column(9) int age %2f "Age on last birthday"
_column(11) int school %1f "How far did you go in school"
_column(12) int monthsoversea %1f "How many months spent overseas"
_column(13) int honoluluactivities %1f "Trouble finding interesting things in Honolulu"
_column(14) int honoluluvisit %1f "How often do you go to Honolulu"
_column(15) int gasmasktype %1f "New or Old Type"
_column(16) int gastrainingimportance %1f "How important is training in protection against gas attacks"
_column(17) int gasmaskusage %1f "How often have you worn your gas mask on duty"
_column(18) int gasmaskusageopinion %1f "Should soldiers wear their gas mask once per week"
_column(19) int gaschamber %1f "How often have you been through a gas chamber"
_column(20) int gaschamberopinion %1f "Should soldiers go through a gas chamber once a month"
_column(21) int gasmaskconf %1f "Confidence in gas mask given a gas attack"
_column(22) int japangasattack %1f "If Japan uses gas in an attack, can you protect yourself"
_column(23) int gasusageopinion %1f "Should we use gas against Japan"
_column(24) int japangasopinion %1f "How likely is Japan to use gas against us"
_column(25) int japangasamerknowledge %1f "Do you know if Japan has used gas against American troops"
_column(26) int japangasaustknowledge %1f "Do you know if Japan has used gas against Australian troops"
_column(27) int japangasbritknowledge %1f "Do you know if Japan has used gas against British troops"
_column(28) int japangaschinknowledge %1f "Do you know if Japan has used gas against Chinese troops"
_column(29) int orientmeetopinion %1f "Are so-called orientation meetings worthwhile"
_column(30) int orientmeetfreq %1f "How often do you attend orientation meetings"
_column(31) int orientmeetleadopinion %1f "Would you prefer an officer or an elisted man lead orientation meetings"
_column(32) int oriemtmeetknowledge %1f "Does the presenter deliver the materials effectively"
_column(33) int orientmeetinterest %1f "How many orientation meetings have been interesting"
_column(34) int orientmeetteach %1f "Do orientation meetings teach why we are fighting the war"
_column(35) int usaffairsinterest %1f "How interested are you in what is going on in the US"
_column(36) int usprobinterest %1f "How interested are you in US problems post-war"
_column(37) int usworldaffairsinterest %1f "How interested are you in US,s part in world affairs post-war"
_column(38) int ussoldierretireinterest %1f "How interested are you in plans for retiring US soldiers"
_column(39) int enemyfactsinterest %1f "How interested are you in facts about our enemy: Germany and Japan"
_column(40) int allyfactsinterest %1f "How interested are you in facts about our allies: England, Russia, China, etc"
_column(41) int warjapaninterest %1f "How interested are you in the progress of the war against Japan"
_column(42) int wargermaninterest %1f "How interested are you in the progress of the war against Germany"
_column(43) int peaceprobinteret %1f "How interested are you in problems of the peace"
_column(44) int centpacificinterest %1f "How interested are you in the wartime importance of the central pacific"
_column(45) int postmeetchat %1f "How often do the men chat about the material after an orientation meeting"
_column(46) int orientmeetpartic %1f "Do men usually participate at orientation meetings"
_column(47) int orientmeetparticfreq %1f "How often do you participate if given the chance at orientation meetings"
_column(48) int orientmeetofficerattend %1f "Do company officers go to orientation meetings"
_column(49) int meetdiscussionofficers %1f "Do men participate more or less when officers attend the meetings"
_column(50) int warintcentnear %1f "Is there a war information center in or near your outfit"
_column(51) int warintcentusage %1f "How often do you use the war information center"
_column(52) int warintcenthelpful %1f "How informative are the maps at the war information center"
_column(53) int offdutycourseaware %1f "Have you been told anything about off-duty educational courses"
_column(54) int offdutycourseinterest %1f "Are you interested in taking a US Armed Forces Institute course"
_column(55) int calisthenimportant %1f "How important is calisthenics in your training program"
_column(56) int swimimportant %1f "How important is learning to swim in your training program"
_column(57) int orientimportant %1f "How important is hearing orientation talks in your training program"
_column(58) int gasmaskimportant %1f "How important is weekly gas mask usage in your training program"
_column(59) int drillimportant %1f "How important is close order drill in your training program"
_column(60) int guncareimportant %1f "How important is care and field-stripping guns in your training program"
_column(61) int dryfireimportant %1f "How important is dry firing in your training program"
_column(62) int rangefireimportant %1f "How important is firing on range in your training program"
_column(63) int firstaidimportant %1f "How important is first aid in your training program"
_column(64) int gaschamberimportant %1f "How important is going through gas chamber in your training program"
_column(65) int hikesimportant %1f "How important are hikes in your training program"
_column(66) int obstcourseimportant %1f "How important are obstacle courses in your training program"
_column(67) int chinarelatpostwar %1f "How do you think we will get along with China post-war"
_column(68) int russrelatpostwar %1f "How do you think we will get along with Russia post-war"
_column(69) int englrelatpostwar %1f "How do you think we will get along with England post-war"
_column(70) int schedorientmeet %1f "How often is the reported schedule of orientation meetings"
_column(71) int basechemwartrain %1f "Did the air base have chemical warfare training"
_column(72) int serialnumstation %3f "Serial #s within station"
_column(75) int notused %6f "Not used"
}


(648 observations read)

. 
. cd "$dir"
/Users/christopherblair/Desktop/JOP Replication

. sort cardnum surveynum ballotnum

. save "${dir}/ams175.dta", replace
file ~/Desktop/JOP Replication/ams175.dta saved

. 
. drop notused

. 
. label define rank 0 "No Answer" 1 "PRV. or PFC." 2 "CPL or TCH5" 3 "SGT or TCH4" 4 "SSGT or TCH3" 5 "TSGT, MSGT, 1SGT"

. label val rankgrade rank

. label define age 0 "No Answer" 1 "18 or Less" 2 "19" 3 "20" 4 "21" 5 "22" 6 "23" 7 "24" 8 "25" 9 "26-29" 10 "30-34" 11 "35+"

. label val age age

. label define edu 0 "No Answer" 1 "<8th Grade" 2 "8th Grade" 3 "Some High School" 4 "High School" 5 "College"

. label val school edu

. label define time 0 "No Answer" 1 "3 Months or Less" 2 "3-6 Months" 3 "6-9 Months" 4 "9-12 Months" 5 "12-18 Months" 6 "18-24 Months
> " 7 "24-30 Months" 8 "30-36 Months" 9 "36+ Months"

. label val monthsoversea time

. label define activities 0 "No Answer" 1 "Yes, I do" 2 "No, I don't"

. label val honoluluactivities activities

. label define honolulu 0 "No Answer" 1 "Every time" 2 "About half the time" 3 "Just once in awhile" 4 "Hardly ever"

. label val honoluluvisit honolulu

. label define masktype 0 "No Answer" 1 "New Type Mask" 2 "Old Type Mask" 3 "Not Sure" 4 "No Mask"

. label val gasmasktype masktype

. label define gastrain 0 "No Answer" 1 "Absolutely Necessary" 2 "Great Importance" 3 "Medium Importance" 4 "Little Importance" 5 "No
>  Importance"

. label val gastrainingimportance gastrain

. label define wear 0 "No Answer" 1 "1" 2 "2" 3 "3" 4 "4" 5 "0"

. label val gasmaskusage wear

. label define wearopinion 0 "No Answer" 1 "Very Good Idea" 2 "Fairly Good Idea" 3 "Very Poor Idea" 4 "Poor Idea"

. label val gasmaskusageopinion wearopinion

. label define chamber 1 "1" 2 "2" 3 "3+" 4 "0"

. label val gaschamber chamber

. label define chamberopinion 0 "No Answer" 1 "Very Good Idea" 2 "Fairly Good Idea" 3 "Very Poor Idea" 4 "Poor Idea"

. label val gaschamberopinion chamberopinion

. label define maskconf 0 "No Answer" 1 "A Great Deal" 2 "A Fair Amount" 3 "Not So Much" 4 "Hardly Any"

. label val gasmaskconf maskconf

. label define protect 0 "No Answer" 1 "Yes" 2 "Yes, but not sure" 3 "No" 4 "Undecided"

. label val japangasattack protect

. label define use 0 "No Answer" 1 "Use Gas by Surprise" 2 "Use Gas if Japan Does" 3 "Should Not Use Gas" 4 "Undecided"

. label val gasusageopinion use

. label define likely 0 "No Answer" 1 "Very Likely" 2 "Fairly Likely" 3 "Not so Likely" 4 "Not Likely" 5 "Undecided"

. label val japangasopinion likely

. label define diduse 0 "No Answer" 1 "Yes" 2 "No" 3 "Don't Know"

. label val japangasamerknowledge diduse

. label val japangasaustknowledge diduse

. label val japangasbritknowledge diduse

. label val japangaschinknowledge diduse

. label define worth 0 "No Answer" 1 "Very Much Worthwhile" 2 "Fairly Worthwhile" 3 "Not So Worthwhile" 4 "Not Worthwhile" 5 "Undecid
> ed"

. label val orientmeetopinion worth

. label define attend 0 "No Answer" 1 "< Weekly" 2 "Weekly" 3 "> Weekly" 4 "Never" 5 "No Meetings Held"

. label val orientmeetfreq attend

. label define lead 0 "No Answer" 1 "Officer" 2 "Enlisted Man" 3 "Makes No Difference"

. label val orientmeetleadopinion lead

. label define knowstuff 0 "No Answer" 1 "Always" 2 "Usually" 3 "Seldom" 4 "Never" 5 "No Meetings Held"

. label val oriemtmeetknowledge knowstuff

. label define meetinterest 0 "No Answer" 1 "All" 2 "Most" 3 "Half" 4 "Few" 5 "None" 6 "No Meetings Held"

. label val orientmeetinterest meetinterest

. label define teach 0 "No Answer" 1 "Alot" 2 "Somewhat" 3 "Hardly at all" 4 "No Meetings Held"

. label val orientmeetteach teach

. label define interest 0 "No Answer" 1 "Very Interested" 2 "Not so Interested" 3 "Not Interested at All"

. label val usaffairsinterest interest

. label val usprobinterest interest

. label val usworldaffairsinterest interest

. label val ussoldierretireinterest interest

. label val enemyfactsinterest interest

. label val allyfactsinterest interest

. label val warjapaninterest interest

. label val wargermaninterest interest

. label val peaceprobinteret interest

. label val centpacificinterest interest

. label define discuss 0 "No Answer" 1 "Always" 2 "Usually" 3 "Seldom" 4 "Never" 5 "No Meetings Held"

. label val postmeetchat discuss

. label define share 0 "No Answer" 1 "Yes" 2 "No"

. label val orientmeetpartic share

. label val orientmeetofficerattend share

. label define giveopinion 0 "No Answer" 1 "Often" 2 "Sometimes" 3 "Seldom" 4 "Never" 5 "No Chance for Input"

. label val orientmeetparticfreq giveopinion

. label define officertalk 0 "No Answer" 1 "More Discussion When Officers Attend" 2 "Less Discussion When Officers Attend" 3 "Makes N
> o Difference" 4 "No Chance for Input"

. label val meetdiscussionofficers officertalk

. label define infocenter 0 "No Answer" 1 "Yes" 2 "No" 3 "Don't Know"

. label val warintcentnear infocenter

. label define infocenterfreq 0 "No Answer" 1 "< Weekly" 2 "Once a Week" 3 "1-2 Times a Week" 4 "3-4 Times a Week" 5 "Daily" 6 "Never
> " 7 "No Information Center"

. label val warintcentusage infocenterfreq

. label define infohelp 0 "No Answer" 1 "Alot" 2 "Somewhat" 3 "Hardly at All" 4 "No Information Center"

. label val warintcenthelpful infohelp

. label define toldclass 0 "No Answer" 1 "Yes" 2 "No" 3 "Not Sure"

. label val offdutycourseaware toldclass

. label define takeclass 0 "No Answer" 1 "Yes, in course" 2 "Yes, interested" 3 "No"

. label val offdutycourseinterest takeclass

. label define important 0 "No Answer" 1 "Very Important" 2 "Fairly Important" 3 "Not So Important" 4 "Not Important At All"

. label val calisthenimportant important

. label val swimimportant important

. label val orientimportant important

. label val gasmaskimportant important

. label val drillimportant important

. label val guncareimportant important

. label val dryfireimportant important

. label val rangefireimportant important

. label val firstaidimportant important

. label val gaschamberimportant important

. label val hikesimportant important

. label val obstcourseimportant important

. label define allies 0 "No Answer" 1 "Very Well" 2 "Disagree, But Get Along" 3 "Disagree, But Won't Fight" 4 "Will Fight" 5 "Undecid
> ed"

. label val chinarelatpostwar allies

. label val russrelatpostwar allies

. label val englrelatpostwar allies

. label define schedule 0 "No Answer" 1 "< Weekly" 2 "Once a Week" 3 "> Weekly" 4 "Not At All" 5 "Don't Know"

. label val schedorientmeet schedule

. label define basetrain 1 "Hickam -- CW Training" 2 "Hickam -- No Training" 3 "Wheeler -- No Training" 4 "Mokuleia -- CW Training" 5
>  "Bellows -- CW Training" 6 "Bellows -- No Training" 7 "Kahulu -- CW Training" 8 "Kahulu -- No Training"

. label val basechemwartrain basetrain

. 
. save "${dir}/ams175.dta", replace
file ~/Desktop/JOP Replication/ams175.dta saved

. 
. 
. ** Generate Key Variables for Analysis
. 
. gen use=(gasusageopinion==1 | gasusageopinion==2)

. replace use=. if gasusageopinion==0
(14 real changes made, 14 to missing)

. label var use "US Should Use CW (=1)"

. 
. gen dontuse=(gasusageopinion==3 | gasusageopinion==4)

. replace dontuse=. if gasusageopinion==0
(14 real changes made, 14 to missing)

. label var dontuse "US Should Not Use CW/Undecided (=1)"

. 
. gen firstuse=(gasusageopinion==1)

. replace firstuse=. if gasusageopinion==0
(14 real changes made, 14 to missing)

. label var firstuse "US Should Use CW Preemptively (=1)"

. 
. gen seconduse=(gasusageopinion==2)

. replace seconduse=. if gasusageopinion==0
(14 real changes made, 14 to missing)

. label var seconduse "US Should Use CW in Retaliation (=1)"

. 
. gen undecided=(gasusageopinion==4)

. replace undecided=. if gasusageopinion==0
(14 real changes made, 14 to missing)

. label var undecided "Not Sure if US Should Use CW (=1)"

. 
. gen refrain=(gasusageopinion==3)

. replace refrain=. if gasusageopinion==0
(14 real changes made, 14 to missing)

. label var refrain "US Should Not Use CW (=1)"

. 
. gen use3=.
(648 missing values generated)

. replace use3=2 if gasusageopinion==1
(150 real changes made)

. replace use3=1 if gasusageopinion==2
(425 real changes made)

. replace use3=0 if gasusageopinion==3 | gasusageopinion==4
(59 real changes made)

. label var use3 "Support for CW Use (3 Categories)"

. 
. gen use4=.
(648 missing values generated)

. replace use4=3 if gasusageopinion==1
(150 real changes made)

. replace use4=2 if gasusageopinion==2
(425 real changes made)

. replace use4=1 if gasusageopinion==4
(49 real changes made)

. replace use4=0 if gasusageopinion==3
(10 real changes made)

. label var use4 "Support for CW Use (4 Categories)"

. 
. gen base=.
(648 missing values generated)

. replace base=1 if basechemwartrain==1 | basechemwartrain==2
(164 real changes made)

. replace base=2 if basechemwartrain==3
(69 real changes made)

. replace base=3 if basechemwartrain==4
(66 real changes made)

. replace base=4 if basechemwartrain==5 | basechemwartrain==6
(172 real changes made)

. replace base=5 if basechemwartrain==7 | basechemwartrain==8
(177 real changes made)

. label define base 1 "Hickam" 2 "Wheeler" 3 "Mokuleia" 4 "Bellows" 5 "Kahulu"

. label val base base

. label var base "Base"

. 
. gen training=0

. replace training=1 if basechemwartrain==1 | basechemwartrain==4 | basechemwartrain==5 | basechemwartrain==7
(280 real changes made)

. label define training 0 "No Training" 1 "CW Training"

. label val training training

. label var training "Training Regimen"

. 
. gen mask=(inrange(gasmaskusage,1,4))

. label var mask "Used Gas Mask (=1)"

. gen nomask=(gasmaskusage==5)

. label var nomask "Never Used Gas Mask (=1)"

. gen mask_nr=(gasmaskusage==0)

. label var mask_nr "Gas Mask Non-Response (=1)"

. 
. gen nummask=0

. replace nummask=1 if gasmaskusage==1
(59 real changes made)

. replace nummask=2 if gasmaskusage==2
(48 real changes made)

. replace nummask=3 if gasmaskusage==3
(53 real changes made)

. replace nummask=4 if gasmaskusage==4
(253 real changes made)

. label var nummask "Gas Mask Frequency"

. 
. gen nummask_miss=.
(648 missing values generated)

. replace nummask_miss=0 if gasmaskusage==5
(228 real changes made)

. replace nummask_miss=1 if gasmaskusage==1
(59 real changes made)

. replace nummask_miss=2 if gasmaskusage==2
(48 real changes made)

. replace nummask_miss=3 if gasmaskusage==3
(53 real changes made)

. replace nummask_miss=4 if gasmaskusage==4
(253 real changes made)

. label var nummask_miss "Gas Mask Frequency (Non-Response Dropped)"

. 
. gen chamber=(inrange(gaschamber,1,3))

. label var chamber "Used Gas Chamber (=1)"

. gen nochamber=(gaschamber==4)

. label var nochamber "Never Used Gas Chamber (=1)"

. 
. gen numchamber=0

. replace numchamber=1 if gaschamber==1
(295 real changes made)

. replace numchamber=2 if gaschamber==2
(128 real changes made)

. replace numchamber=3 if gaschamber==3
(73 real changes made)

. label var numchamber "Gas Chamber Frequency"

. 
. gen multiple_chamber=(numchamber>=2 & numchamber!=.)

. label var multiple_chamber "Multiple Exposure to Gas Chamber (=1)"

. 
. gen newmask=(gasmasktype==1)

. label var newmask "New Gas Mask (=1)"

. 
. gen confidence3=.
(648 missing values generated)

. replace confidence3=0 if gasmaskconf==4 | gasmaskconf==3
(12 real changes made)

. replace confidence3=1 if gasmaskconf==2
(100 real changes made)

. replace confidence3=2 if gasmaskconf==1
(525 real changes made)

. gen highconfid=(gasmaskconf==1)

. replace highconfid=. if gasmaskconf==0
(11 real changes made, 11 to missing)

. gen lowconfid=(gasmaskconf==4 | gasmaskconf==3)

. replace lowconfid=. if gasmaskconf==0
(11 real changes made, 11 to missing)

. replace highconfid=. if gasmaskconf==0
(0 real changes made)

. label var confidence3 "Confidence in Gas Mask"

. label var lowconfid "Low Confidence in Gas Mask (=1)"

. label var highconfid "High Confidence in Gas Mask (=1)"

. 
. gen protect3=.
(648 missing values generated)

. replace protect3=0 if japangasattack==4 | japangasattack==3
(15 real changes made)

. replace protect3=1 if japangasattack==2
(100 real changes made)

. replace protect3=2 if japangasattack==1
(522 real changes made)

. gen highprotect=(japangasattack==1)

. replace highprotect=. if japangasattack==0
(11 real changes made, 11 to missing)

. gen lowprotect=(japangasattack==4 | japangasattack==3)

. replace lowprotect=. if japangasattack==0
(11 real changes made, 11 to missing)

. replace highprotect=. if japangasattack==0
(0 real changes made)

. label var protect3 "Protect Yourself in Gas Attack"

. label var lowprotect "Low Confidence in Protect Yourself (=1)"

. label var highprotect "High Confidence in Protect Yourself (=1)"

. 
. gen gaslikelyuse=(japangasopinion==1 | japangasopinion==2)

. gen us_use=(japangasamerknowledge ==1)

. gen aus_use=(japangasaustknowledge ==1)

. gen brit_use=(japangasbritknowledge ==1)

. gen china_use=(japangaschinknowledge ==1)

. label var gaslikelyuse "Japan Likely to Use CW (=1)"

. label var us_use "Japan Used CW on US (=1)"

. label var aus_use "Japan Used CW on Australia (=1)"

. label var brit_use "Japan Used CW on UK (=1)"

. label var china_use "Japan Used CW on China (=1)"

. swindex us_use aus_use brit_use china_use, gen(use_history)

. label var use_history "Previous Use Against Allies (ICW)"

. gen correct_history=(us_use==0 & aus_use==0 & brit_use==0 & china_use==1)

. label var correct_history "Knows History of Use Against Allies (=1)"

. 
. gen orient_worth=(orientmeetopinion==1 | orientmeetopinion==2)

. label var orient_worth "Orientation Meetings Worthwhile (=1)"

. gen orient_meet=(orientmeetfreq==2 | orientmeetfreq==3)

. label var orient_meet "Attend Orientation Meetings Weekly (=1)"

. gen orient_nomeet=(orientmeetfreq==5)

. label var orient_nomeet "No Orientation Meetings Held (=1)"

. gen orient_offlead=(orientmeetleadopinion==1)

. label var orient_offlead "Prefer Officers Lead Orientation Meetings (=1)"

. gen orient_enllead=(orientmeetleadopinion==2)

. label var orient_enllead "Prefer Enlisted Men Lead Orientation Meetings (=1)"

. gen orient_offattend=(orientmeetofficerattend==1)

. label var orient_offattend "Officers Attend Orientation Meetings (=1)"

. gen officers_talk=(meetdiscussionofficers==1)

. label var officers_talk "More Discussion When Officers Attend (=1)"

. gen orient_effective=(oriemtmeetknowledge==1 | oriemtmeetknowledge==2)

. label var orient_effective "Orientation Meetings Effective (=1)"

. gen orient_interest=(orientmeetinterest==1 | orientmeetinterest==2)

. label var orient_interest "Orientation Meetings Interesting (=1)"

. gen orient_teach=(orientmeetteach==1)

. label var orient_teach "Orientation Meetings Teach About War (=1)"

. gen orient_partic=(orientmeetpartic==1)

. label var orient_partic "Men Participate in Orientation Meetings (=1)"

. gen orient_youpartic=(orientmeetparticfreq==1 | orientmeetparticfreq==2)

. label var orient_youpartic "You Participate in Orientation Meetings (=1)"

. gen orient_impt=(orientimportant==1 | orientimportant==2)

. label var orient_impt "Orientation Meetings Important (=1)"

. gen orient_talk=(postmeetchat==1 | postmeetchat==2)

. label var orient_talk "Men Chat After Orientation (=1)"

. gen orient_sched=(schedorientmeet==2 | schedorientmeet==3)

. label var orient_sched "Orientation Meetings Scheduled Weekly (=1)"

. swindex orient_meet orient_partic orient_youpartic orient_talk orient_sched, gen(orientation_particp)

. label var orientation_particp "Orientation Participation (ICW)"

. swindex orient_worth orient_interest orient_teach orient_impt orient_effective, gen(orientation_learn)

. label var orientation_learn "Orientation Learning (ICW)"

. swindex orient_offlead orient_offattend officers_talk, gen(orientation_officers)

. label var orientation_officers "Officers' Leadership (ICW)"

. 
. gen interest1=(usaffairsinterest==1)

. gen interest2=(usprobinterest==1)

. gen interest3=(usworldaffairsinterest==1)

. gen interest4=(ussoldierretireinterest==1)

. gen interest5=(enemyfactsinterest==1)

. gen interest6=(allyfactsinterest==1)

. gen interest7=(warjapaninterest==1)

. gen interest8=(wargermaninterest==1)

. gen interest9=(peaceprobinteret==1)

. gen interest10=(centpacificinterest==1)

. swindex interest1-interest10, gen(general_interest)

. label var general_interest "General News Interest (ICW)"

. swindex interest3 interest5-interest10, gen(war_interest)

. label var war_interest "War News Interest (ICW)"

. drop interest1-interest10

. 
. gen infocenter=(warintcentnear==1)

. label var infocenter "Information Center (=1)"

. gen noinfocenter=(warintcentnear==2)

. label var noinfocenter "No Information Center (=1)"

. gen infocenter_visit=(warintcentusage==5 | warintcentusage==4)

. label var infocenter_visit "Use Information Center 3+ Times Weekly (=1)"

. gen infocenter_help=(warintcenthelpful==1)

. label var infocenter_help "Information Center if Helpful (=1)"

. swindex infocenter infocenter_visit infocenter_help, gen(information_center)

. label var information_center "Information Center Access (ICW)"

. 
. gen courses1=(offdutycourseaware==1)

. gen courses2=(offdutycourseinterest==1 | offdutycourseinterest==2)

. gen incourse=(offdutycourseinterest==1)

. label var incourse "In Off-Duty Course (=1)"

. swindex courses1 courses2, gen(offduty_courses)

. drop courses1 courses2

. label var offduty_courses "Off-Duty Course Interest (ICW)"

. 
. gen honolulu1=(honoluluactivities==2)

. gen honolulu2=(honoluluvisit==1)

. swindex honolulu1 honolulu2, gen(honolulu_contact)

. drop honolulu1 honolulu2

. label var honolulu_contact "Local Contact (ICW)"

. 
. gen goodgastrain=(gastrainingimportance==1 | gastrainingimportance==2)

. gen goodmasktrain=(gasmaskusageopinion==1 | gasmaskusageopinion==2)

. gen maskimpt=(gasmaskimportant==1 | gasmaskimportant==2)

. gen goodchambertrain=(gaschamberopinion==1 | gaschamberopinion==2)

. gen chamberimpt=(gaschamberimportant==1 | gaschamberimportant==2)

. swindex goodgastrain goodmasktrain goodchambertrain chamberimpt, gen(gas_training)

. drop goodgastrain goodmasktrain goodchambertrain

. label var maskimpt "Important to Train in Gas Masks (=1)"

. label var chamberimpt "Important to Train in Gas Chamber (=1)"

. label var gas_training "Gas Training Favorability (ICW)"

. 
. gen training1=(calisthenimportant==1 | calisthenimportant==2)

. gen training2=(swimimportant==1 | swimimportant==2)

. gen training3=(drillimportant==1 | drillimportant==2)

. gen training4=(guncareimportant==1 | guncareimportant==2)

. gen training5=(dryfireimportant==1 | dryfireimportant==2)

. gen training6=(rangefireimportant==1 | rangefireimportant==2)

. gen training7=(firstaidimportant==1 | firstaidimportant==2)

. gen training8=(hikesimportant==1 | hikesimportant==2)

. gen training9=(obstcourseimportant==1 | obstcourseimportant==2)

. swindex training1 training2 training8 training9, gen(physical_training)

. swindex training3-training7, gen(military_training)

. drop training1-training9

. label var physical_training "Physical Fitness Training (ICW)"

. label var military_training "Military Skills Training (ICW)"

. 
. gen china_relations=(chinarelatpostwar==1)

. gen ussr_relations=(russrelatpostwar==1)

. gen brit_relations=(englrelatpostwar==1)

. swindex china_relations ussr_relations brit_relations, gen(ally_relations)

. drop china_relations ussr_relations brit_relations

. label var ally_relations "Postwar Ally Relations (ICW)"

. 
. save "${dir}/ams175.dta", replace
file ~/Desktop/JOP Replication/ams175.dta saved

. 
. ************************************ IPTW **************************************
. 
. eststo clear

. 
. global core "rankgrade age school monthsoversea"

. global addit "war_interest orient_meet infocenter orientation_officers honolulu_contact"

. probit multiple_chamber i.($core) $addit, cluster(base)

Iteration 0:   log pseudolikelihood = -401.27319  
Iteration 1:   log pseudolikelihood =  -341.3268  
Iteration 2:   log pseudolikelihood = -340.82245  
Iteration 3:   log pseudolikelihood = -340.82192  
Iteration 4:   log pseudolikelihood = -340.82192  

Probit regression                               Number of obs     =        648
                                                Wald chi2(3)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -340.82192               Pseudo R2         =     0.1506

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
    multiple_chamber |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
           rankgrade |
       PRV. or PFC.  |  -.9383928   .4086972    -2.30   0.022    -1.739425    -.137361
        CPL or TCH5  |  -1.005724   .3196828    -3.15   0.002    -1.632291   -.3791574
        SGT or TCH4  |  -1.100324   .3627102    -3.03   0.002    -1.811223    -.389425
       SSGT or TCH3  |  -.8126283   .5331557    -1.52   0.127    -1.857594    .2323377
   TSGT, MSGT, 1SGT  |  -.4470708   .4700747    -0.95   0.342      -1.3684    .4742586
                     |
                 age |
         18 or Less  |  -.4364245   1.064553    -0.41   0.682     -2.52291    1.650061
                 19  |  -.3316454   .3779364    -0.88   0.380    -1.072387    .4090963
                 20  |  -.4986838   .2265399    -2.20   0.028    -.9426939   -.0546738
                 21  |  -.1869933   .3189905    -0.59   0.558    -.8122032    .4382166
                 22  |  -.8618284   .2310212    -3.73   0.000    -1.314622   -.4090352
                 23  |  -.5345742   .2799535    -1.91   0.056    -1.083273    .0141245
                 24  |  -.5112794   .3664995    -1.40   0.163    -1.229605    .2070464
                 25  |  -.6750079   .1246768    -5.41   0.000      -.91937   -.4306458
              26-29  |  -.6339915   .1620873    -3.91   0.000    -.9516768   -.3163063
              30-34  |  -.5624086   .2916121    -1.93   0.054    -1.133958    .0091406
                35+  |  -.3861257     .26826    -1.44   0.150    -.9119056    .1396543
                     |
              school |
         <8th Grade  |  -.7967764   .4267534    -1.87   0.062    -1.633198    .0396449
          8th Grade  |  -.6697857   .2895017    -2.31   0.021    -1.237199   -.1023728
   Some High School  |  -1.000157   .3940744    -2.54   0.011    -1.772529   -.2277853
        High School  |  -.7432333   .2566867    -2.90   0.004     -1.24633   -.2401366
            College  |  -.8323558   .4178489    -1.99   0.046    -1.651325   -.0133869
                     |
       monthsoversea |
   3 Months or Less  |   .5601632   .5318235     1.05   0.292    -.4821918    1.602518
         3-6 Months  |  -.6736099   .6424446    -1.05   0.294    -1.932778    .5855583
         6-9 Months  |   .2055812   .5277674     0.39   0.697    -.8288239    1.239986
        9-12 Months  |  -.0244918   .3651082    -0.07   0.947    -.7400907     .691107
       12-18 Months  |   .2710005   .5009643     0.54   0.589    -.7108715    1.252873
       18-24 Months  |   .6005746   .6428352     0.93   0.350    -.6593593    1.860509
       24-30 Months  |   .2048408   .5817282     0.35   0.725    -.9353255    1.345007
       30-36 Months  |   .5541938   1.013551     0.55   0.585    -1.432329    2.540717
         36+ Months  |    .055793    .580441     0.10   0.923     -1.08185    1.193436
                     |
        war_interest |   -.038525   .0589763    -0.65   0.514    -.1541165    .0770666
         orient_meet |   .6787431   .1627728     4.17   0.000     .3597143     .997772
          infocenter |   .4705562   .1606307     2.93   0.003     .1557259    .7853866
orientation_officers |   .1446975   .0940061     1.54   0.124    -.0395511    .3289461
    honolulu_contact |   .0117687   .0567339     0.21   0.836    -.0994277    .1229651
               _cons |   .8925514   1.191368     0.75   0.454    -1.442487     3.22759
--------------------------------------------------------------------------------------

. predict pr_multiple, pr

. gen iptw=.
(648 missing values generated)

. replace iptw=1/pr_multiple if multiple_chamber==1
(201 real changes made)

. replace iptw=1/(1-pr_multiple) if multiple_chamber==0
(447 real changes made)

. drop pr_multiple

. label var iptw "Inverse Probability of Treatment Weights"

. 
. eststo clear

. 
. *********************************** COMPLIANCE *********************************
. 
. gen complier=0

. replace complier=1 if numchamber>0 & training==1
(247 real changes made)

. replace complier=1 if numchamber==0 & training==1 & monthsoversea==1
(1 real change made)

. replace complier=1 if numchamber==0 & nummask>0 & training==0 & nummask!=.
(87 real changes made)

. replace complier=1 if numchamber==0 & nummask==0 & training==0 & nummask!=. & monthsoversea==1
(8 real changes made)

. label var complier "Complied With Treatment Assignment (=1)"

. 
. eststo clear

. 
. global core "rankgrade age school monthsoversea"

. global addit "training orientation_officers"

. probit complier i.($core) $addit i.base, cluster(base)

Iteration 0:   log pseudolikelihood = -448.04454  
Iteration 1:   log pseudolikelihood =  -246.9967  
Iteration 2:   log pseudolikelihood = -240.71914  
Iteration 3:   log pseudolikelihood = -240.63738  
Iteration 4:   log pseudolikelihood = -240.63734  
Iteration 5:   log pseudolikelihood = -240.63734  

Probit regression                               Number of obs     =        648
                                                Wald chi2(3)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -240.63734               Pseudo R2         =     0.4629

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
            complier |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
           rankgrade |
       PRV. or PFC.  |   .2998752   .5609501     0.53   0.593    -.7995668    1.399317
        CPL or TCH5  |   .3529158   .6391682     0.55   0.581    -.8998308    1.605662
        SGT or TCH4  |   .2704641   .6713536     0.40   0.687    -1.045365    1.586293
       SSGT or TCH3  |  -.7208461   .6468739    -1.11   0.265    -1.988696    .5470034
   TSGT, MSGT, 1SGT  |  -.5134169   .4946517    -1.04   0.299    -1.482916    .4560825
                     |
                 age |
         18 or Less  |  -.1547169   1.029034    -0.15   0.880    -2.171587    1.862153
                 19  |   1.371463   .4814607     2.85   0.004     .4278178    2.315109
                 20  |   1.097079   .5026326     2.18   0.029     .1119368     2.08222
                 21  |   .5613259   .4249134     1.32   0.186     -.271489    1.394141
                 22  |    1.34144   .3285272     4.08   0.000     .6975379    1.985341
                 23  |   .8481196   .3233786     2.62   0.009     .2143092     1.48193
                 24  |   1.405535   .3896204     3.61   0.000     .6418928    2.169177
                 25  |   .7759024   .3991253     1.94   0.052    -.0063688    1.558174
              26-29  |   .9580394   .4453388     2.15   0.031     .0851914    1.830887
              30-34  |   .9501423   .4378079     2.17   0.030     .0920546     1.80823
                35+  |   1.034977   .5141347     2.01   0.044     .0272912    2.042662
                     |
              school |
         <8th Grade  |  -1.183577   .2706834    -4.37   0.000    -1.714106   -.6530469
          8th Grade  |  -1.197758   .3941826    -3.04   0.002    -1.970342   -.4251747
   Some High School  |  -.8210626   .2923116    -2.81   0.005    -1.393983   -.2481425
        High School  |  -.9678741   .4295811    -2.25   0.024    -1.809838   -.1259106
            College  |  -.8445094   .6288197    -1.34   0.179    -2.076973    .3879546
                     |
       monthsoversea |
   3 Months or Less  |   .8977689   .2248794     3.99   0.000     .4570133    1.338524
         3-6 Months  |   1.069321   .5389217     1.98   0.047     .0130535    2.125588
         6-9 Months  |   .3693687   .5470535     0.68   0.500    -.7028364    1.441574
        9-12 Months  |   1.142961   .2649318     4.31   0.000     .6237045    1.662218
       12-18 Months  |   .8145866   .5488126     1.48   0.138    -.2610663     1.89024
       18-24 Months  |   .4050719    .355353     1.14   0.254    -.2914072    1.101551
       24-30 Months  |   .4671128   .4699041     0.99   0.320    -.4538823    1.388108
       30-36 Months  |   .4969985   .6433718     0.77   0.440     -.763987    1.757984
         36+ Months  |   .7635729   .5939521     1.29   0.199    -.4005519    1.927698
                     |
            training |   2.946858   .5528864     5.33   0.000     1.863221    4.030496
orientation_officers |   .1207335   .0988431     1.22   0.222    -.0729954    .3144624
                     |
                base |
            Wheeler  |   .4529878   .1806134     2.51   0.012     .0989922    .8069835
           Mokuleia  |  -1.304121   .4110031    -3.17   0.002    -2.109672   -.4985698
            Bellows  |  -.4411151   .2090272    -2.11   0.035    -.8508009   -.0314293
             Kahulu  |   .6473469    .108057     5.99   0.000     .4355591    .8591348
                     |
               _cons |  -1.993508   1.058309    -1.88   0.060    -4.067756    .0807395
--------------------------------------------------------------------------------------

. predict pr_complier, pr

. label var pr_complier "Probability of Treatment Compliance"

. 
. eststo clear

. 
. *********************************** ENTROPY ************************************
. 
. eststo clear

. 
. gen somehs=(school>=3)

. gen somecollege=(school==5)

. gen age1525=(inrange(age,1,8))

. gen age2634=(inrange(age,9,10))

. gen age35=(age==11)

. 
. ebalance somehs somecollege age1525 age2634 age35, manual(0.568 0.123 0.499 0.413 0.088) generate(demweight)


Data Setup
Covariate adjustment: somehs somecollege age1525 age2634 age35


Optimizing...
Iteration 1: Max Difference = 709.12
Iteration 2: Max Difference = 260.638417
Iteration 3: Max Difference = 95.651744
Iteration 4: Max Difference = 34.957838
Iteration 5: Max Difference = 12.6332784
Iteration 6: Max Difference = 4.42962525
Iteration 7: Max Difference = 1.43387844
Iteration 8: Max Difference = .377697132
Iteration 9: Max Difference = .058149328
Iteration 10: Max Difference = .002185438
maximum difference smaller than the tolerance level; convergence achieved


No. of units adjusted: 648 total of weights: 648


Before: without weighting

             |      mean   variance   skewness 
-------------+---------------------------------
      somehs |     .7531      .1862     -1.174 
 somecollege |     .1265      .1107      2.247 
     age1525 |     .5077      .2503    -.03087 
     age2634 |      .375      .2347      .5164 
       age35 |     .1096     .09771        2.5 


After:  demweight as the weighting variable

             |      mean   variance   skewness 
-------------+---------------------------------
      somehs |     .5684      .2457     -.2763 
 somecollege |     .1229       .108      2.297 
     age1525 |     .4987      .2504    .005237 
     age2634 |     .4126      .2427      .3549 
       age35 |    .08805     .08042      2.907 

. label var demweight "Demographic Entropy Weights"

. drop somehs somecollege age1525 age2634 age35

. 
. ************************************* CEM **************************************
. 
. cem age(0(1)11) school(0(1)5) monthsoversea(0(1)9) rankgrade(0(1)5) infocenter(0 1) orient_meet(0 1) training(0 1) war_interest, tr
> eatment(chamber)

Matching Summary:
-----------------
Number of strata: 10
Number of matched strata: 10

             0    1
      All  152  496
  Matched  152  496
Unmatched    0    0


Multivariate L1 distance: .98387097

Univariate imbalance:

                    L1     mean      min      25%      50%      75%      max
          age   .14682   .59291       -2        0        2        1        0
       school   .06499  -.05823        0       -1        0        0        0
monthsoversea   .27886   .47431       -1        0        2        0        0
    rankgrade   .07183   .11524       -1        0        0        0        0
   infocenter   .07606   .07606        0        0        0        0        0
  orient_meet   .38947   .38947        0        0        1        1        0
     training   .29021   .29021        0        0        0        1        0
 war_interest   .03743   .00028        0   .06505  -.05894        0        0

. 
. label var cem_strata "CEM Strata"

. label var cem_matched "CEM Matched =(1)"

. label var cem_weights "CEM Weights"

. ren cem_strata cem_strata1

. ren cem_matched cem_matched1

. ren cem_weights cem_weights1

. 
. cem age(0(1)11) school(0(1)5) monthsoversea(0(1)9) rankgrade(0(1)5) infocenter(0 1) orient_meet(0 1) training(0 1) war_interest, tr
> eatment(multiple)
(using the scott break method for imbalance)

Matching Summary:
-----------------
Number of strata: 10
Number of matched strata: 10

             0    1
      All  447  201
  Matched  447  201
Unmatched    0    0


Multivariate L1 distance: .98352229

Univariate imbalance:

                    L1     mean      min      25%      50%      75%      max
          age   .05992  -.09397        0        0        0        0        0
       school   .08316  -.11725        0       -1        0        0        0
monthsoversea   .14006   .18239        0        0        1        0        0
    rankgrade   .10285  -.11931        0        0        0        0        0
   infocenter    .2323    .2323        0        0        1        0        0
  orient_meet   .30893   .30893        0        1        1        0        0
     training   .25035   .25035        0        0        1        0        0
 war_interest   .05542  -.00769        0   .05581  -.09978        0        0

. 
. label var cem_strata "CEM Strata"

. label var cem_matched "CEM Matched =(1)"

. label var cem_weights "CEM Weights"

. ren cem_strata cem_strata2

. ren cem_matched cem_matched2

. ren cem_weights cem_weights2

. 
. ********************************* ADDITIONAL ***********************************
. 
. gen lowedu=.
(648 missing values generated)

. replace lowedu=4 if school==1
(73 real changes made)

. replace lowedu=3 if school==2
(84 real changes made)

. replace lowedu=2 if school==3
(182 real changes made)

. replace lowedu=1 if school==4
(224 real changes made)

. replace lowedu=0 if school==5
(82 real changes made)

. replace lowedu=0 if school==0
(3 real changes made)

. label var lowedu "Education Scale (Reverse)"

. 
. gen use_comply=use
(14 missing values generated)

. replace use_comply=. if complier==0
(300 real changes made, 300 to missing)

. label var use_comply "US Should Use CW (=1) Conditional on Compliance"

. 
. ********************************************************************************
. 
. save "${dir}/ams175.dta", replace
file ~/Desktop/JOP Replication/ams175.dta saved

. 
. foreach x of var age school rankgrade monthsoversea base {
  2. tab `x', gen(`x')
  3. }

Age on last |
   birthday |      Freq.     Percent        Cum.
------------+-----------------------------------
  No Answer |          5        0.77        0.77
 18 or Less |          2        0.31        1.08
         19 |         12        1.85        2.93
         20 |         46        7.10       10.03
         21 |         44        6.79       16.82
         22 |         64        9.88       26.70
         23 |         52        8.02       34.72
         24 |         69       10.65       45.37
         25 |         40        6.17       51.54
      26-29 |        120       18.52       70.06
      30-34 |        123       18.98       89.04
        35+ |         71       10.96      100.00
------------+-----------------------------------
      Total |        648      100.00

 How far did you |
    go in school |      Freq.     Percent        Cum.
-----------------+-----------------------------------
       No Answer |          3        0.46        0.46
      <8th Grade |         73       11.27       11.73
       8th Grade |         84       12.96       24.69
Some High School |        182       28.09       52.78
     High School |        224       34.57       87.35
         College |         82       12.65      100.00
-----------------+-----------------------------------
           Total |        648      100.00

    What is your |
    army rank or |
           grade |      Freq.     Percent        Cum.
-----------------+-----------------------------------
       No Answer |          8        1.23        1.23
    PRV. or PFC. |        256       39.51       40.74
     CPL or TCH5 |        185       28.55       69.29
     SGT or TCH4 |        121       18.67       87.96
    SSGT or TCH3 |         54        8.33       96.30
TSGT, MSGT, 1SGT |         24        3.70      100.00
-----------------+-----------------------------------
           Total |        648      100.00

 How many months |
  spent overseas |      Freq.     Percent        Cum.
-----------------+-----------------------------------
       No Answer |          3        0.46        0.46
3 Months or Less |         58        8.95        9.41
      3-6 Months |         35        5.40       14.81
      6-9 Months |         23        3.55       18.36
     9-12 Months |        131       20.22       38.58
    12-18 Months |         75       11.57       50.15
    18-24 Months |        103       15.90       66.05
    24-30 Months |        142       21.91       87.96
    30-36 Months |         18        2.78       90.74
      36+ Months |         60        9.26      100.00
-----------------+-----------------------------------
           Total |        648      100.00

       Base |      Freq.     Percent        Cum.
------------+-----------------------------------
     Hickam |        164       25.31       25.31
    Wheeler |         69       10.65       35.96
   Mokuleia |         66       10.19       46.14
    Bellows |        172       26.54       72.69
     Kahulu |        177       27.31      100.00
------------+-----------------------------------
      Total |        648      100.00

. 
. save "${dir}/ams175_R.dta", replace
file ~/Desktop/JOP Replication/ams175_R.dta saved

. 
. ********************************** DEMOGRAPHICS ********************************
. 
. eststo clear

. 
. gen somehs=(school==3)

. gen somecollege=(school==5)

. gen age1525=(inrange(age,1,8))

. gen age2634=(inrange(age,9,10))

. gen age35=(age==11)

. 
. sum age1525 age2634 age35 somehs somecollege monthsoversea

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
     age1525 |        648     .507716    .5003267          0          1
     age2634 |        648        .375    .4844969          0          1
       age35 |        648    .1095679    .3125917          0          1
      somehs |        648    .2808642    .4497685          0          1
 somecollege |        648    .1265432    .3327174          0          1
-------------+---------------------------------------------------------
monthsover~a |        648    5.234568    2.257854          0          9

. sum age1525 age2634 age35 somehs somecollege monthsoversea if chamber==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
     age1525 |        496    .4778226    .5000122          0          1
     age2634 |        496     .391129    .4884959          0          1
       age35 |        496    .1209677     .326419          0          1
      somehs |        496    .2782258    .4485776          0          1
 somecollege |        496    .1310484     .337794          0          1
-------------+---------------------------------------------------------
monthsover~a |        496    5.346774    2.238486          0          9

. sum age1525 age2634 age35 somehs somecollege monthsoversea if chamber==0

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
     age1525 |        152    .6052632    .4904099          0          1
     age2634 |        152    .3223684    .4689282          0          1
       age35 |        152    .0723684    .2599535          0          1
      somehs |        152    .2894737    .4550173          0          1
 somecollege |        152    .1118421     .316214          0          1
-------------+---------------------------------------------------------
monthsover~a |        152    4.868421     2.28929          1          9

. 
. clear

. 
end of do-file

. 
. if c(username) == "christopherblair"{
. global dir "~/Desktop/JOP Replication"
. global raw "${dir}/Raw Files"
. global code "${dir}/Code"                                                                               
. }

. cd "${dir}"
/Users/christopherblair/Desktop/JOP Replication

. 
. do "${code}/CW Polls.do"

. ********************************************************************************
. *                                                        CIVILIAN POLLS DATA                                                       
> *
. ********************************************************************************
. 
. clear all

. set more off

. set scheme plotplainblind

. macro drop _all

. est drop _all

. set matsize 800

. set seed 8675309

. 
. ** Set Working Directory
. 
. if c(username) == "christopherblair"{
. global dir "~/Desktop/JOP Replication"
. global raw "${dir}/Raw Files"
. global code "${dir}/Code"
. global result "${dir}/Results"
. global poll "${dir}/Gallup Polls"                                                                               
. }

. 
. else if c(username) == "youruser"{
. global dir "~/Desktop/JOP Replication"
. global raw "${dir}/Raw Files"
. global code "${dir}/Code"
. global result "${dir}/Results"
. global poll "${dir}/Gallup Polls"       
. }

. 
. ********************************************************************************
. 
. eststo clear

. 
. ** September 1944
. 
. use "${poll}/1944 - September/USAIPO1944-0329.dta"

. 
. gen cw_japan=(Q2AP==1)

. replace cw_japan=. if Q2AP==.c
(3,019 real changes made, 3,019 to missing)

. replace cw_japan=. if Q2AP==.a
(22 real changes made, 22 to missing)

. label var cw_japan "Approve CW Use Against Japanese Cities to Expedite War End: Yes"

. 
. gen cw_germany=(Q2BP==1)

. replace cw_germany=. if Q2BP==.c
(3,019 real changes made, 3,019 to missing)

. replace cw_germany=. if Q2BP==.a
(24 real changes made, 24 to missing)

. label var cw_germany "Approve CW Use Against German Cities to Expedite War End: Yes"

. 
. gen all=1

. 
. eststo: reg cw_japan all [pw= WtPubEd], cluster(state) nocons
(sum of wgt is 2,725.0323630509)

Linear regression                               Number of obs     =      2,726
                                                F(1, 47)          =     252.80
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2384
                                                Root MSE          =     .42619

                                 (Std. Err. adjusted for 48 clusters in state)
------------------------------------------------------------------------------
             |               Robust
    cw_japan |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         all |   .2384135   .0149947    15.90   0.000      .208248    .2685789
------------------------------------------------------------------------------
(est1 stored)

. eststo: reg cw_germany all [pw= WtPubEd], cluster(state) nocons
(sum of wgt is 2,726.35914640005)

Linear regression                               Number of obs     =      2,723
                                                F(1, 47)          =     142.30
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1711
                                                Root MSE          =     .37667

                                 (Std. Err. adjusted for 48 clusters in state)
------------------------------------------------------------------------------
             |               Robust
  cw_germany |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         all |   .1711061   .0143436    11.93   0.000     .1422504    .1999617
------------------------------------------------------------------------------
(est2 stored)

. 
. ** December 1944
. 
. use "${poll}/1944 - December/USAIPO1944-0337.dta", clear

. 
. gen cw_japan=(Q1==1)

. replace cw_japan=. if Q1==.c
(0 real changes made)

. replace cw_japan=. if Q1==.a
(20 real changes made, 20 to missing)

. label var cw_japan "Approve CW Use Against Japanese Cities in Retaliation for Killing Bomber Pilots: Yes"
note: label truncated to 80 characters

. 
. gen all=1

. 
. eststo: reg cw_japan all [pw= WtPubEd], cluster(state) nocons
(sum of wgt is 2,706.4240249958)

Linear regression                               Number of obs     =      2,710
                                                F(1, 47)          =     667.62
                                                Prob > F          =     0.0000
                                                R-squared         =     0.4625
                                                Root MSE          =     .49869

                                 (Std. Err. adjusted for 48 clusters in state)
------------------------------------------------------------------------------
             |               Robust
    cw_japan |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         all |   .4625267   .0179008    25.84   0.000      .426515    .4985384
------------------------------------------------------------------------------
(est3 stored)

. 
. ** March 1945
. 
. use "${poll}/1945 - March/USAIPO1945-0343.dta", clear

. 
. gen cw_japan_k=(Q2K==1)

. replace cw_japan_k=. if Q2K==.c
(1,482 real changes made, 1,482 to missing)

. replace cw_japan_k=. if Q2K==.a
(9 real changes made, 9 to missing)

. label var cw_japan_k "Approve CW Use Against Japanese Soldiers to Expedite War End: Yes"

. 
. gen cw_japan_t=(Q2AT==1)

. replace cw_japan_t=. if Q2AT==.c
(1,523 real changes made, 1,523 to missing)

. replace cw_japan_t=. if Q2AT==.a
(12 real changes made, 12 to missing)

. label var cw_japan_t "Approve CW Use Against Japanese Soldiers: Yes"

. 
. gen cw_japan_t_retaliatory=(Q2BT==7)

. replace cw_japan_t_retaliatory=. if Q2BT==.c
(1,523 real changes made, 1,523 to missing)

. replace cw_japan_t_retaliatory=. if Q2BT==.a
(14 real changes made, 14 to missing)

. replace cw_japan_t_retaliatory=. if Q2AT!=1
(1,033 real changes made, 1,033 to missing)

. label var cw_japan_t_retaliatory "US Should Use CW Against Japanese Soldiers: Only if they Use First"

. 
. gen cw_japan=cw_japan_k
(1,491 missing values generated)

. replace cw_japan=cw_japan_t if form==2
(1,470 real changes made)

. 
. gen all=1

. 
. eststo: reg cw_japan all [pw= WtPubEd] if form==1, cluster(state) nocons
(sum of wgt is 1,323.12475397985)

Linear regression                               Number of obs     =      1,353
                                                F(1, 42)          =     254.34
                                                Prob > F          =     0.0000
                                                R-squared         =     0.3903
                                                Root MSE          =     .48799

                                 (Std. Err. adjusted for 43 clusters in state)
------------------------------------------------------------------------------
             |               Robust
    cw_japan |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         all |   .3902589   .0244705    15.95   0.000     .3408754    .4396424
------------------------------------------------------------------------------
(est4 stored)

. 
. eststo: reg cw_japan all [pw= WtPubEd] if form==2, cluster(state) nocons
(sum of wgt is 1,392.9108147652)

Linear regression                               Number of obs     =      1,362
                                                F(1, 44)          =     237.33
                                                Prob > F          =     0.0000
                                                R-squared         =     0.3186
                                                Root MSE          =     .46612

                                 (Std. Err. adjusted for 45 clusters in state)
------------------------------------------------------------------------------
             |               Robust
    cw_japan |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         all |   .3186496   .0206843    15.41   0.000     .2769632    .3603361
------------------------------------------------------------------------------
(est5 stored)

. 
. 
. ** May 1945
. 
. use "${poll}/1945 - May/USAIPO1945-0347.dta", clear

. 
. gen cw_japan=(Q9K==1)

. replace cw_japan=. if Q9K==.c
(1,602 real changes made, 1,602 to missing)

. replace cw_japan=. if Q9K==.a
(12 real changes made, 12 to missing)

. label var cw_japan "Approve CW Use Against Japanese Soldiers to Expedite War End: Yes"

. 
. gen all=1

. 
. eststo: reg cw_japan all [pw= WtPubEd], cluster(state) nocons
(sum of wgt is 1,458.63603986349)

Linear regression                               Number of obs     =      1,435
                                                F(1, 44)          =     268.26
                                                Prob > F          =     0.0000
                                                R-squared         =     0.3189
                                                Root MSE          =     .46622

                                 (Std. Err. adjusted for 45 clusters in state)
------------------------------------------------------------------------------
             |               Robust
    cw_japan |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         all |   .3189123   .0194713    16.38   0.000     .2796705     .358154
------------------------------------------------------------------------------
(est6 stored)

. 
. ** June 1945
. 
. use "${poll}/1945 - June/USAIPO1945-0349.dta", clear

. 
. gen cw_japan_k=(Q3AK==1)

. replace cw_japan_k=. if Q3AK==.c
(1,548 real changes made, 1,548 to missing)

. replace cw_japan_k=. if Q3AK==.a
(7 real changes made, 7 to missing)

. label var cw_japan_k "Approve CW Use Against Japanese to Reduce US Casualties: Yes"

. 
. gen cw_japan_t=(Q3AT==1)

. replace cw_japan_t=. if Q3AT==.c
(1,587 real changes made, 1,587 to missing)

. replace cw_japan_t=. if Q3AT==.a
(3 real changes made, 3 to missing)

. label var cw_japan_t "Approve CW Use Against Japanese to Reduce US Casualties: Yes"

. 
. gen cw_japan=cw_japan_k
(1,555 missing values generated)

. replace cw_japan=cw_japan_t if form==2
(1,545 real changes made)

. 
. gen all=1

. 
. eststo: reg cw_japan all [pw= WtPubEd] if form==1, cluster(state) nocons
(sum of wgt is 1,462.9293533424)

Linear regression                               Number of obs     =      1,437
                                                F(1, 44)          =     520.03
                                                Prob > F          =     0.0000
                                                R-squared         =     0.4198
                                                Root MSE          =     .49369

                                 (Std. Err. adjusted for 45 clusters in state)
------------------------------------------------------------------------------
             |               Robust
    cw_japan |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         all |   .4197571    .018407    22.80   0.000     .3826603     .456854
------------------------------------------------------------------------------
(est7 stored)

. 
. eststo: reg cw_japan all [pw= WtPubEd] if form==2, cluster(state) nocons
(sum of wgt is 1,289.67324167503)

Linear regression                               Number of obs     =      1,315
                                                F(1, 43)          =     310.06
                                                Prob > F          =     0.0000
                                                R-squared         =     0.4115
                                                Root MSE          =      .4923

                                 (Std. Err. adjusted for 44 clusters in state)
------------------------------------------------------------------------------
             |               Robust
    cw_japan |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         all |   .4115493   .0233723    17.61   0.000     .3644146     .458684
------------------------------------------------------------------------------
(est8 stored)

. 
. eststo: reg cw_japan all [pw= WtPubEd], cluster(state) nocons
(sum of wgt is 2,752.60259501744)

Linear regression                               Number of obs     =      2,752
                                                F(1, 46)          =     638.93
                                                Prob > F          =     0.0000
                                                R-squared         =     0.4159
                                                Root MSE          =     .49297

                                 (Std. Err. adjusted for 47 clusters in state)
------------------------------------------------------------------------------
             |               Robust
    cw_japan |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         all |   .4159115   .0164541    25.28   0.000     .3827911     .449032
------------------------------------------------------------------------------
(est9 stored)

. 
. 
. ********************************************************************************
. 
. coefplot (est1, msym(O) mfcolor(white) mlcolor(black) msize(large) ciopts(lwidth(.5 1.15) lcolor(black black))) (est3, msym(D) mfco
> lor(white) mlcolor(black) msize(large) ciopts(lwidth(.5 1.15) lcolor(black black))) (est4, msym(O) mfcolor(red) mlcolor(red) msize(
> large) ciopts(lwidth(.5 1.15) lcolor(red red))) (est5, msym(S) mfcolor(red) mlcolor(red) msize(large) ciopts(lwidth(.5 1.15) lcolor
> (red red))) (est6, msym(O) mfcolor(red) mlcolor(red) msize(large) ciopts(lwidth(.5 1.15) lcolor(red red))) (est9, msym(O) mfcolor(r
> ed) mlcolor(red) msize(large) ciopts(lwidth(.5 1.15) lcolor(red red))), vert ylabel(.1(.05).5) ymtick(.1(.01).5) legend(off) ytitle
> ("Average Civilian Support for Using" "Chemical Weapons Against Japan") title(" ") xline(.71375, lpatt(solid) lcolor(gs10)) xlabel(
> .6425 `""September" "1944""' .785 `""December" "1944""' .9277 `""March" "1945""' 1.0686 `" "March" "1945""' 1.213 `""May" "1945""' 
> 1.356`""June" "1945""') text(.2384135 .6 ".238", size(small)) text(.4625267 .74 ".463", size(small)) text(.3902589 .89 ".390", size
> (small)) text(.3186496 1.03 ".319", size(small)) text(.3189123 1.18 ".319", size(small)) text(.4159115  1.32 ".416", size(small)) t
> ext(.5 .68 `"S-175"', size(small)) ci(95 90)

. 
. graph export "${result}/gallup_cw.png", replace
(file ~/Desktop/JOP Replication/Results/gallup_cw.png written in PNG format)

. 
. 
. ********************************************************************************
. 
. eststo clear

. 
. ** September 1944
. 
. use "${poll}/1944 - September/USAIPO1944-0329.dta", clear

. 
. gen cw_japan=(Q2AP==1)

. replace cw_japan=. if Q2AP==.c
(3,019 real changes made, 3,019 to missing)

. replace cw_japan=. if Q2AP==.a
(22 real changes made, 22 to missing)

. label var cw_japan "Approve CW Use Against Japanese Cities to Expedite War End: Yes"

. 
. gen cw_germany=(Q2BP==1)

. replace cw_germany=. if Q2BP==.c
(3,019 real changes made, 3,019 to missing)

. replace cw_germany=. if Q2BP==.a
(24 real changes made, 24 to missing)

. label var cw_germany "Approve CW Use Against German Cities to Expedite War End: Yes"

. 
. gen all=1

. 
. eststo: reg cw_japan black [pw= WtPubEd], cluster(state)
(sum of wgt is 2,725.0323630509)

Linear regression                               Number of obs     =      2,726
                                                F(1, 47)          =       0.13
                                                Prob > F          =     0.7245
                                                R-squared         =     0.0001
                                                Root MSE          =     .42624

                                 (Std. Err. adjusted for 48 clusters in state)
------------------------------------------------------------------------------
             |               Robust
    cw_japan |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       black |   .0179055    .050493     0.35   0.724    -.0836733    .1194843
       _cons |   .2368232   .0154358    15.34   0.000     .2057704     .267876
------------------------------------------------------------------------------
(est1 stored)

. 
. ** December 1944
. 
. use "${poll}/1944 - December/USAIPO1944-0337.dta", clear

. 
. gen cw_japan=(Q1==1)

. replace cw_japan=. if Q1==.c
(0 real changes made)

. replace cw_japan=. if Q1==.a
(20 real changes made, 20 to missing)

. label var cw_japan "Approve CW Use Against Japanese Cities in Retaliation for Killing Bomber Pilots: Yes"
note: label truncated to 80 characters

. 
. gen all=1

. 
. eststo: reg cw_japan black [pw= WtPubEd], cluster(state)
(sum of wgt is 2,706.4240249958)

Linear regression                               Number of obs     =      2,710
                                                F(1, 47)          =       2.01
                                                Prob > F          =     0.1629
                                                R-squared         =     0.0023
                                                Root MSE          =     .49819

                                 (Std. Err. adjusted for 48 clusters in state)
------------------------------------------------------------------------------
             |               Robust
    cw_japan |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       black |   .0849969   .0599563     1.42   0.163    -.0356196    .2056134
       _cons |   .4550138   .0184999    24.60   0.000     .4177967    .4922308
------------------------------------------------------------------------------
(est2 stored)

. 
. ** March 1945
. 
. use "${poll}/1945 - March/USAIPO1945-0343.dta", clear

. 
. gen cw_japan_k=(Q2K==1)

. replace cw_japan_k=. if Q2K==.c
(1,482 real changes made, 1,482 to missing)

. replace cw_japan_k=. if Q2K==.a
(9 real changes made, 9 to missing)

. label var cw_japan_k "Approve CW Use Against Japanese Soldiers to Expedite War End: Yes"

. 
. gen cw_japan_t=(Q2AT==1)

. replace cw_japan_t=. if Q2AT==.c
(1,523 real changes made, 1,523 to missing)

. replace cw_japan_t=. if Q2AT==.a
(12 real changes made, 12 to missing)

. label var cw_japan_t "Approve CW Use Against Japanese Soldiers: Yes"

. 
. gen cw_japan_t_retaliatory=(Q2BT==7)

. replace cw_japan_t_retaliatory=. if Q2BT==.c
(1,523 real changes made, 1,523 to missing)

. replace cw_japan_t_retaliatory=. if Q2BT==.a
(14 real changes made, 14 to missing)

. replace cw_japan_t_retaliatory=. if Q2AT!=1
(1,033 real changes made, 1,033 to missing)

. label var cw_japan_t_retaliatory "US Should Use CW Against Japanese Soldiers: Only if they Use First"

. 
. gen cw_japan=cw_japan_k
(1,491 missing values generated)

. replace cw_japan=cw_japan_t if form==2
(1,470 real changes made)

. 
. gen all=1

. 
. eststo: reg cw_japan black [pw= WtPubEd] if form==1, cluster(state)
(sum of wgt is 1,323.12475397985)

Linear regression                               Number of obs     =      1,353
                                                F(1, 42)          =       2.18
                                                Prob > F          =     0.1477
                                                R-squared         =     0.0058
                                                Root MSE          =     .48675

                                 (Std. Err. adjusted for 43 clusters in state)
------------------------------------------------------------------------------
             |               Robust
    cw_japan |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       black |   .1348248   .0914091     1.47   0.148    -.0496462    .3192957
       _cons |   .3790727   .0245991    15.41   0.000     .3294297    .4287158
------------------------------------------------------------------------------
(est3 stored)

. 
. eststo: reg cw_japan black [pw= WtPubEd] if form==2, cluster(state)
(sum of wgt is 1,392.9108147652)

Linear regression                               Number of obs     =      1,362
                                                F(1, 44)          =       1.35
                                                Prob > F          =     0.2513
                                                R-squared         =     0.0015
                                                Root MSE          =     .46594

                                 (Std. Err. adjusted for 45 clusters in state)
------------------------------------------------------------------------------
             |               Robust
    cw_japan |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       black |   .0627221   .0539494     1.16   0.251    -.0460058      .17145
       _cons |   .3128161   .0218818    14.30   0.000     .2687163    .3569159
------------------------------------------------------------------------------
(est4 stored)

. 
. 
. ** May 1945
. 
. use "${poll}/1945 - May/USAIPO1945-0347.dta", clear

. 
. gen cw_japan=(Q9K==1)

. replace cw_japan=. if Q9K==.c
(1,602 real changes made, 1,602 to missing)

. replace cw_japan=. if Q9K==.a
(12 real changes made, 12 to missing)

. label var cw_japan "Approve CW Use Against Japanese Soldiers to Expedite War End: Yes"

. 
. gen all=1

. 
. eststo: reg cw_japan black [pw= WtPubEd], cluster(state)
(sum of wgt is 1,458.63603986349)

Linear regression                               Number of obs     =      1,435
                                                F(1, 44)          =      12.50
                                                Prob > F          =     0.0010
                                                R-squared         =     0.0119
                                                Root MSE          =     .46359

                                 (Std. Err. adjusted for 45 clusters in state)
------------------------------------------------------------------------------
             |               Robust
    cw_japan |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       black |   .1797768   .0508558     3.54   0.001     .0772836    .2822699
       _cons |   .3030823   .0205756    14.73   0.000     .2616148    .3445497
------------------------------------------------------------------------------
(est5 stored)

. 
. 
. ** June 1945
. 
. use "${poll}/1945 - June/USAIPO1945-0349.dta", clear

. 
. gen cw_japan_k=(Q3AK==1)

. replace cw_japan_k=. if Q3AK==.c
(1,548 real changes made, 1,548 to missing)

. replace cw_japan_k=. if Q3AK==.a
(7 real changes made, 7 to missing)

. label var cw_japan_k "Approve CW Use Against Japanese to Reduce US Casualties: Yes"

. 
. gen cw_japan_t=(Q3AT==1)

. replace cw_japan_t=. if Q3AT==.c
(1,587 real changes made, 1,587 to missing)

. replace cw_japan_t=. if Q3AT==.a
(3 real changes made, 3 to missing)

. label var cw_japan_t "Approve CW Use Against Japanese to Reduce US Casualties: Yes"

. 
. gen cw_japan=cw_japan_k
(1,555 missing values generated)

. replace cw_japan=cw_japan_t if form==2
(1,545 real changes made)

. 
. gen all=1

. 
. eststo: reg cw_japan black [pw= WtPubEd] if form==1, cluster(state)
(sum of wgt is 1,462.9293533424)

Linear regression                               Number of obs     =      1,437
                                                F(1, 44)          =       0.29
                                                Prob > F          =     0.5941
                                                R-squared         =     0.0005
                                                Root MSE          =     .49375

                                 (Std. Err. adjusted for 45 clusters in state)
------------------------------------------------------------------------------
             |               Robust
    cw_japan |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       black |   .0365846   .0681528     0.54   0.594    -.1007683    .1739375
       _cons |    .416408   .0185787    22.41   0.000     .3789651     .453851
------------------------------------------------------------------------------
(est6 stored)

. 
. eststo: reg cw_japan black [pw= WtPubEd] if form==2, cluster(state)
(sum of wgt is 1,289.67324167503)

Linear regression                               Number of obs     =      1,315
                                                F(1, 43)          =       1.94
                                                Prob > F          =     0.1710
                                                R-squared         =     0.0034
                                                Root MSE          =     .49166

                                 (Std. Err. adjusted for 44 clusters in state)
------------------------------------------------------------------------------
             |               Robust
    cw_japan |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       black |   .1023586   .0735116     1.39   0.171    -.0458916    .2506089
       _cons |   .4028665   .0234619    17.17   0.000     .3555512    .4501819
------------------------------------------------------------------------------
(est7 stored)

. 
. eststo: reg cw_japan black [pw= WtPubEd], cluster(state)
(sum of wgt is 2,752.60259501744)

Linear regression                               Number of obs     =      2,752
                                                F(1, 46)          =       1.76
                                                Prob > F          =     0.1916
                                                R-squared         =     0.0015
                                                Root MSE          =      .4927

                                 (Std. Err. adjusted for 47 clusters in state)
------------------------------------------------------------------------------
             |               Robust
    cw_japan |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       black |   .0664383   .0501252     1.33   0.192    -.0344586    .1673352
       _cons |   .4100386   .0169779    24.15   0.000     .3758639    .4442133
------------------------------------------------------------------------------
(est8 stored)

. 
. 
. ********************************************************************************
. 
. coefplot (est1, msym(O) mfcolor(white) mlcolor(black) msize(large) ciopts(lwidth(.5 1.15) lcolor(black black))) (est2, msym(D) mfco
> lor(white) mlcolor(black) msize(large) ciopts(lwidth(.5 1.15) lcolor(black black))) (est3, msym(O) mfcolor(red) mlcolor(red) msize(
> large) ciopts(lwidth(.5 1.15) lcolor(red red))) (est4, msym(S) mfcolor(red) mlcolor(red) msize(large) ciopts(lwidth(.5 1.15) lcolor
> (red red))) (est5, msym(O) mfcolor(red) mlcolor(red) msize(large) ciopts(lwidth(.5 1.15) lcolor(red red))) (est8, msym(O) mfcolor(r
> ed) mlcolor(red) msize(large) ciopts(lwidth(.5 1.15) lcolor(red red))), vert keep(black) ci(95 90) ylabel(-.15(.05).35) ymtick(-.15
> (.01).35) legend(off) yline(0, lpatt(shortdash) lcolor(navy)) xline(.71375, lpatt(solid) lcolor(gs10)) yline(-.15, lpatt(dot) lcolo
> r(gs10)) yline(.35, lpatt(dot) lcolor(gs10)) ytitle("Racial Gap in" "Average Civilian Support for Using" "Chemical Weapons Against 
> Japan") title(" ") xlabel(.6425 `""September" "1944""' .785 `""December" "1944""' .9277 `""March" "1945""' 1.0686 `" "March" "1945"
> "' 1.213 `""May" "1945""' 1.356`""June" "1945""') text(.025 .59 ".018", size(small)) text(.098 .741 ".085", size(small)) text(.14 .
> 88 ".135", size(small)) text(.07 1.02 ".063", size(small)) text(.19 1.17 ".180", size(small)) text(.07  1.31 ".066", size(small)) t
> ext(.35 .75 `"S-175"', size(small))

. 
. graph export "${result}/gallup_cw_black.png", replace
(file ~/Desktop/JOP Replication/Results/gallup_cw_black.png written in PNG format)

. 
. ********************************************************************************
. 
. clear all

. 
end of do-file

. 
. if c(username) == "christopherblair"{
. global dir "~/Desktop/JOP Replication"
. global raw "${dir}/Raw Files"
. global code "${dir}/Code"                                                                               
. }

. cd "${dir}"
/Users/christopherblair/Desktop/JOP Replication

. 
. do "${code}/CW Main.do"

. ********************************************************************************
. *                                                CHEMICAL WEAPONS ANALYSIS                                                         
> *
. ********************************************************************************
. 
. clear all

. set more off

. set scheme plotplainblind

. macro drop _all

. est drop _all

. set matsize 800

. set seed 8675309

. 
. ** Set Working Directory
. 
. if c(username) == "christopherblair"{
. global dir "~/Desktop/JOP Replication"
. global raw "${dir}/Raw Files"
. global code "${dir}/Code"
. global result "${dir}/Results"                                                                  
. }

. 
. else if c(username) == "youruser"{
. global dir "~/Desktop/JOP Replication"
. global raw "${dir}/Raw Files"
. global code "${dir}/Code"
. global result "${dir}/Results"  
. }

. 
. cd "$raw"
/Users/christopherblair/Desktop/JOP Replication/Raw Files

. 
. ********************************************************************************
. 
. use "${dir}/ams175.dta", clear

. 
. sort ballotnum

. 
. global demographic "age school monthsoversea rankgrade"

. global covariates "ally_relations orientation_officers infocenter orient_meet war_interest honolulu_contact"

. 
. ********************************************************************************
. *                                                               GRAPHICAL RESULTS                                                  
>         *
. ********************************************************************************
. 
. collapse (mean) meanuse= use (sd) sduse=use (count) n=use, by(numchamber)

. generate hiuse = meanuse + invttail(n-1,0.025)*(sduse / sqrt(n))

. generate lowuse = meanuse - invttail(n-1,0.025)*(sduse / sqrt(n))

. 
. twoway (bar meanuse numchamber, fcolor(white) lcolor(black)) (rcap hiuse lowuse numchamber, lcolor(black)), ylabel(.7(.05)1) ymtick
> (.7(.01)1) yline(1, lcolor(gs10) lpatt(dot)) yline(.9069401, lcolor(cranberry) lpatt(shortdash)) ytitle("Average Support for Using"
>  "Chemical Weapons Against Japan") xtitle("Times Training w/ Gas Exposure") legend(off) text(.925 -.22 "0.933", size(small)) text(.
> 897 .82 "0.907", size(small)) text(.8875 1.82 "0.897", size(small)) text(.865 2.82 "0.871", size(small))

. graph export "${result}/mean_chamber_ci.png", replace
(file ~/Desktop/JOP Replication/Results/mean_chamber_ci.png written in PNG format)

. 
. use "${dir}/ams175.dta", clear

. 
. sort ballotnum

. 
. global demographic "age school monthsoversea rankgrade"

. global covariates "ally_relations orientation_officers infocenter orient_meet war_interest honolulu_contact"

. 
. collapse (mean) meanuse= use (sd) sduse=use (count) n=use, by(nummask)

. generate hiuse = meanuse + invttail(n-1,0.025)*(sduse / sqrt(n))

. generate lowuse = meanuse - invttail(n-1,0.025)*(sduse / sqrt(n))

. 
. twoway (bar meanuse nummask, fcolor(white) lcolor(black)) (rcap hiuse lowuse nummask, lcolor(black)), ylabel(.7(.05)1) ymtick(.7(.0
> 1)1) yline(1, lcolor(gs10) lpatt(dot)) yline(.9069401, lcolor(cranberry) lpatt(shortdash)) ytitle("Average Support for Using" "Chem
> ical Weapons Against Japan") xtitle("Times Training in Gas Masks") legend(off) text(.8875 -.22 "0.899", size(small)) text(.818 .82 
> "0.828", size(small)) text(.968 1.82 "0.979", size(small)) text(.897 2.82 "0.906", size(small)) text(.91 3.81 "0.919", size(small))

. graph export "${result}/mean_mask_ci.png", replace
(file ~/Desktop/JOP Replication/Results/mean_mask_ci.png written in PNG format)

. 
. 
. ********************************************************************************
. *                                                               SUMMARY STATISTICS                                                 
>         *
. ********************************************************************************
. 
. use "${dir}/ams175.dta", clear

. 
. sort ballotnum

. 
. global demographic "age school monthsoversea rankgrade"

. global covariates "ally_relations orientation_officers infocenter orient_meet war_interest honolulu_contact"

. 
. foreach x of var school age monthsoversea rankgrade {
  2. tab `x', gen(`x')
  3. }

 How far did you |
    go in school |      Freq.     Percent        Cum.
-----------------+-----------------------------------
       No Answer |          3        0.46        0.46
      <8th Grade |         73       11.27       11.73
       8th Grade |         84       12.96       24.69
Some High School |        182       28.09       52.78
     High School |        224       34.57       87.35
         College |         82       12.65      100.00
-----------------+-----------------------------------
           Total |        648      100.00

Age on last |
   birthday |      Freq.     Percent        Cum.
------------+-----------------------------------
  No Answer |          5        0.77        0.77
 18 or Less |          2        0.31        1.08
         19 |         12        1.85        2.93
         20 |         46        7.10       10.03
         21 |         44        6.79       16.82
         22 |         64        9.88       26.70
         23 |         52        8.02       34.72
         24 |         69       10.65       45.37
         25 |         40        6.17       51.54
      26-29 |        120       18.52       70.06
      30-34 |        123       18.98       89.04
        35+ |         71       10.96      100.00
------------+-----------------------------------
      Total |        648      100.00

 How many months |
  spent overseas |      Freq.     Percent        Cum.
-----------------+-----------------------------------
       No Answer |          3        0.46        0.46
3 Months or Less |         58        8.95        9.41
      3-6 Months |         35        5.40       14.81
      6-9 Months |         23        3.55       18.36
     9-12 Months |        131       20.22       38.58
    12-18 Months |         75       11.57       50.15
    18-24 Months |        103       15.90       66.05
    24-30 Months |        142       21.91       87.96
    30-36 Months |         18        2.78       90.74
      36+ Months |         60        9.26      100.00
-----------------+-----------------------------------
           Total |        648      100.00

    What is your |
    army rank or |
           grade |      Freq.     Percent        Cum.
-----------------+-----------------------------------
       No Answer |          8        1.23        1.23
    PRV. or PFC. |        256       39.51       40.74
     CPL or TCH5 |        185       28.55       69.29
     SGT or TCH4 |        121       18.67       87.96
    SSGT or TCH3 |         54        8.33       96.30
TSGT, MSGT, 1SGT |         24        3.70      100.00
-----------------+-----------------------------------
           Total |        648      100.00

. 
. global school "school2 school3 school4 school5 school6"

. global age "age2 age3 age4 age5 age6 age7 age8 age9 age10 age11 age12"

. global rank "rankgrade2 rankgrade3 rankgrade4 rankgrade5 rankgrade6"

. global months "monthsoversea2 monthsoversea3 monthsoversea4 monthsoversea5 monthsoversea6 monthsoversea7 monthsoversea8 monthsovers
> ea9 monthsoversea10"

. global dv "use firstuse seconduse"

. 
. estpost sum $dv numchamber nummask $school $age $rank $months $covariates

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         use |       634        634   .9069401   .0845331   .2907458          0          1        575 
    firstuse |       634        634   .2365931   .1809021   .4253259          0          1        150 
   seconduse |       634        634    .670347    .221331   .4704583          0          1        425 
  numchamber |       648        648   1.188272   .8485794   .9211837          0          3        770 
     nummask |       648        648   2.046296   3.187962   1.785486          0          4       1326 
     school2 |       648        648   .1126543   .1001178    .316414          0          1         73 
     school3 |       648        648   .1296296   .1130002    .336155          0          1         84 
     school4 |       648        648   .2808642   .2022917   .4497685          0          1        182 
     school5 |       648        648    .345679   .2265346   .4759565          0          1        224 
     school6 |       648        648   .1265432   .1107009   .3327174          0          1         82 
        age2 |       648        648   .0030864   .0030816   .0555126          0          1          2 
        age3 |       648        648   .0185185   .0182037    .134921          0          1         12 
        age4 |       648        648   .0709877   .0660503   .2570026          0          1         46 
        age5 |       648        648   .0679012   .0633885   .2517707          0          1         44 
        age6 |       648        648   .0987654   .0891484   .2985773          0          1         64 
        age7 |       648        648   .0802469   .0739214   .2718849          0          1         52 
        age8 |       648        648   .1064815   .0952902   .3086912          0          1         69 
        age9 |       648        648   .0617284   .0580075   .2408475          0          1         40 
       age10 |       648        648   .1851852   .1511248   .3887478          0          1        120 
       age11 |       648        648   .1898148   .1540228   .3924574          0          1        123 
       age12 |       648        648   .1095679   .0977136   .3125917          0          1         71 
  rankgrade2 |       648        648   .3950617   .2393573   .4892416          0          1        256 
  rankgrade3 |       648        648   .2854938   .2043024   .4519982          0          1        185 
  rankgrade4 |       648        648   .1867284   .1520956   .3899944          0          1        121 
  rankgrade5 |       648        648   .0833333    .076507   .2765989          0          1         54 
  rankgrade6 |       648        648    .037037   .0357204   .1889985          0          1         24 
monthsover~2 |       648        648   .0895062   .0816208   .2856935          0          1         58 
monthsover~3 |       648        648   .0540123    .051174   .2262167          0          1         35 
monthsover~4 |       648        648   .0354938   .0342869   .1851673          0          1         23 
monthsover~5 |       648        648   .2021605   .1615409   .4019215          0          1        131 
monthsover~6 |       648        648   .1157407    .102503   .3201609          0          1         75 
monthsover~7 |       648        648   .1589506   .1338919   .3659125          0          1        103 
monthsover~8 |       648        648   .2191358   .1713798   .4139804          0          1        142 
monthsover~9 |       648        648   .0277778   .0270479   .1644625          0          1         18 
monthsove~10 |       648        648   .0925926   .0841491   .2900846          0          1         60 
ally_relat~s |       648        648  -1.63e-18          1          1  -.7221696   2.912098  -1.05e-15 
orientatio~s |       648        648   8.21e-18          1          1  -1.056285   2.766998   5.32e-15 
  infocenter |       648        648    .558642   .2469422   .4969328          0          1        362 
 orient_meet |       648        648   .5385802   .2488957   .4988944          0          1        349 
war_interest |       648        648  -2.51e-17          1          1  -2.561667   .8912646  -1.63e-14 
honolulu_c~t |       648        648  -3.60e-18          1          1  -.6874769   2.533045  -2.33e-15 

. esttab using "${result}/CW_SumStat.tex", style(tex) cells("count(pattern(1 1 0) fmt(0)) mean(pattern(1 1 0) fmt(3)) sd(pattern(1 1 
> 0) fmt(3)) min(pattern(1 1 0) fmt(3)) max(pattern(1 1 0) fmt(3))") varlabels(use "& \\ \textsc{Dependent Variables} \\ \hspace{3mm}
> Support Using Chemical Weapons Against Japan" firstuse "\hspace{3mm}Support First-Use Against Japan" seconduse "\hspace{3mm}Support
>  Second-Use Against Japan" numchamber "& \\ \textsc{Independent Variables} \\ \hspace{3mm}\# of Times Exposed to Gas" nummask "\hsp
> ace{3mm}\# of Times Training w/ Gas Mask" school2 "& \\ \textsc{Control Variables} \\ \hspace{3mm}Schooling: <\nth{8} Grade" school
> 3 "\hspace{3mm}Schooling: \nth{8} Grade" school4 "\hspace{3mm}Schooling: Some High School"  school5 "\hspace{3mm}Schooling: High Sc
> hool" school6 "\hspace{3mm}Schooling: College" age2 "\hspace{3mm}Age: $\leq$18" age3 "\hspace{3mm}Age: 19" age4 "\hspace{3mm}Age: 2
> 0" age5 "\hspace{3mm}Age: 21" age6 "\hspace{3mm}Age: 22" age7 "\hspace{3mm}Age: 23" age8 "\hspace{3mm}Age: 24" age9 "\hspace{3mm}Ag
> e: 25" age10 "\hspace{3mm}Age: 26-29" age11 "\hspace{3mm}Age: 30-34" age12 "\hspace{3mm}Age: 35+" monthsoversea2 "\hspace{3mm}Time 
> Deployed: $\leq$3 Months" monthsoversea3 "\hspace{3mm}Time Deployed: 3-6 Months" monthsoversea4 "\hspace{3mm}Time Deployed: 6-9 Mon
> ths" monthsoversea5 "\hspace{3mm}Time Deployed: 9-12 Months" monthsoversea6 "\hspace{3mm}Time Deployed: 12-18 Months" monthsoversea
> 7 "\hspace{3mm}Time Deployed: 18-24 Months" monthsoversea8 "\hspace{3mm}Time Deployed: 24-30 Months" monthsoversea9 "\hspace{3mm}Ti
> me Deployed: 30-36 Months" monthsoversea10 "\hspace{3mm}Time Deployed: 36+ Months" rankgrade2 "\hspace{3mm}Rank: PRV./PFC." rankgra
> de3 "\hspace{3mm}Rank: CPL./TCH5." rankgrade4 "\hspace{3mm}Rank: SGT./TCH4." rankgrade5 "\hspace{3mm}Rank: SSGT./TCH3." rankgrade6 
> "\hspace{3mm}Rank: TSGT./MSGT./1SGT." ally_relations "\hspace{3mm}Postwar Foreign Policy Index" orientation_officers "\hspace{3mm}O
> fficers' Leadership Index" infocenter "\hspace{3mm}Unit Has War Information Center" orient_meet "\hspace{3mm}Unit Holds Regular Ori
> entation Meetings" war_interest "\hspace{3mm}Interest in War News Index" honolulu_contact "\hspace{3mm}Local Contact Index") noline
> s prehead(\begin{tabular}{l*{7}{c}} \hline & \\) posthead(\hline) prefoot() postfoot(& \\ \hline \end{tabular}) extracols(1) collab
> els("Observations" "Mean" "Std. Dev." "Min" "Max") nomti nonum noobs replace
(output written to ~/Desktop/JOP Replication/Results/CW_SumStat.tex)

. 
. 
. ********************************************************************************
. *                                                               DIFFERENCE-IN-MEANS                                                
>         *
. ********************************************************************************
. 
. eststo sum0: qui estpost summ $school $age $rank $months $covariates if chamber == 0

. eststo sum1: qui estpost summ $school $age $rank $months $covariates if chamber == 1

. eststo diff: qui estpost ttest $school $age $rank $months $covariates, by(chamber)

. 
. esttab sum0 sum1 diff using "${result}/CW_DiffMeans1.tex", style(tex) cells("mean(pattern(1 1 0) fmt(3)) b(star pattern(0 0 1) fmt(
> 3))" "sd(pattern(1 1 0) par)") varlabels(school2 "& \\ \hspace{3mm}Schooling: <\nth{8} Grade" school3 "\hspace{3mm}Schooling: \nth{
> 8} Grade" school4 "\hspace{3mm}Schooling: Some High School" school5 "\hspace{3mm}Schooling: High School" school6 "\hspace{3mm}Schoo
> ling: College" age2 "\hspace{3mm}Age: $\leq$18" age3 "\hspace{3mm}Age: 19" age4 "\hspace{3mm}Age: 20" age5 "\hspace{3mm}Age: 21" ag
> e6 "\hspace{3mm}Age: 22" age7 "\hspace{3mm}Age: 23" age8 "\hspace{3mm}Age: 24" age9 "\hspace{3mm}Age: 25" age10 "\hspace{3mm}Age: 2
> 6-29" age11 "\hspace{3mm}Age: 30-34" age12 "\hspace{3mm}Age: 35+" monthsoversea2 "\hspace{3mm}Time Deployed: $\leq$3 Months" months
> oversea3 "\hspace{3mm}Time Deployed: 3-6 Months" monthsoversea4 "\hspace{3mm}Time Deployed: 6-9 Months" monthsoversea5 "\hspace{3mm
> }Time Deployed: 9-12 Months" monthsoversea6 "\hspace{3mm}Time Deployed: 12-18 Months" monthsoversea7 "\hspace{3mm}Time Deployed: 18
> -24 Months" monthsoversea8 "\hspace{3mm}Time Deployed: 24-30 Months" monthsoversea9 "\hspace{3mm}Time Deployed: 30-36 Months" month
> soversea10 "\hspace{3mm}Time Deployed: 36+ Months" rankgrade2 "\hspace{3mm}Rank: PRV./PFC." rankgrade3 "\hspace{3mm}Rank: CPL./TCH5
> ." rankgrade4 "\hspace{3mm}Rank: SGT./TCH4." rankgrade5 "\hspace{3mm}Rank: SSGT./TCH3." rankgrade6 "\hspace{3mm}Rank: TSGT./MSGT./1
> SGT." ally_relations "\hspace{3mm}Postwar Foreign Policy Index" orientation_officers "\hspace{3mm}Officers' Leadership Index" infoc
> enter "\hspace{3mm}Unit Has War Information Center" orient_meet "\hspace{3mm}Unit Holds Regular Orientation Meetings" war_interest 
> "\hspace{3mm}Interest in War News Index" honolulu_contact "\hspace{3mm}Local Contact Index") nolines prehead(\begin{tabular}{l*{5}{
> c}} \hline & \\) posthead(\hline) prefoot() postfoot(& \\ \hline \end{tabular}) extracols(1 2) collabels(" " " " " ") mti("Not Expo
> sed to Gas" "Exposed to Gas" "Difference-in-Means (Not-Exposed - Exposed)") nonum noobs replace
(output written to ~/Desktop/JOP Replication/Results/CW_DiffMeans1.tex)

. 
. eststo clear

. 
. eststo sum0: qui estpost summ $school $age $rank $months $covariates if multiple == 0

. eststo sum1: qui estpost summ $school $age $rank $months $covariates if multiple == 1

. eststo diff: qui estpost ttest $school $age $rank $months $covariates, by(multiple)

. 
. esttab sum0 sum1 diff using "${result}/CW_DiffMeans2.tex", style(tex) cells("mean(pattern(1 1 0) fmt(3)) b(star pattern(0 0 1) fmt(
> 3))" "sd(pattern(1 1 0) par)") varlabels(school2 "& \\ \hspace{3mm}Schooling: <\nth{8} Grade" school3 "\hspace{3mm}Schooling: \nth{
> 8} Grade" school4 "\hspace{3mm}Schooling: Some High School" school5 "\hspace{3mm}Schooling: High School" school6 "\hspace{3mm}Schoo
> ling: College" age2 "\hspace{3mm}Age: $\leq$18" age3 "\hspace{3mm}Age: 19" age4 "\hspace{3mm}Age: 20" age5 "\hspace{3mm}Age: 21" ag
> e6 "\hspace{3mm}Age: 22" age7 "\hspace{3mm}Age: 23" age8 "\hspace{3mm}Age: 24" age9 "\hspace{3mm}Age: 25" age10 "\hspace{3mm}Age: 2
> 6-29" age11 "\hspace{3mm}Age: 30-34" age12 "\hspace{3mm}Age: 35+" monthsoversea2 "\hspace{3mm}Time Deployed: $\leq$3 Months" months
> oversea3 "\hspace{3mm}Time Deployed: 3-6 Months" monthsoversea4 "\hspace{3mm}Time Deployed: 6-9 Months" monthsoversea5 "\hspace{3mm
> }Time Deployed: 9-12 Months" monthsoversea6 "\hspace{3mm}Time Deployed: 12-18 Months" monthsoversea7 "\hspace{3mm}Time Deployed: 18
> -24 Months" monthsoversea8 "\hspace{3mm}Time Deployed: 24-30 Months" monthsoversea9 "\hspace{3mm}Time Deployed: 30-36 Months" month
> soversea10 "\hspace{3mm}Time Deployed: 36+ Months" rankgrade2 "\hspace{3mm}Rank: PRV./PFC." rankgrade3 "\hspace{3mm}Rank: CPL./TCH5
> ." rankgrade4 "\hspace{3mm}Rank: SGT./TCH4." rankgrade5 "\hspace{3mm}Rank: SSGT./TCH3." rankgrade6 "\hspace{3mm}Rank: TSGT./MSGT./1
> SGT." ally_relations "\hspace{3mm}Postwar Foreign Policy Index" orientation_officers "\hspace{3mm}Officers' Leadership Index" infoc
> enter "\hspace{3mm}Unit Has War Information Center" orient_meet "\hspace{3mm}Unit Holds Regular Orientation Meetings" war_interest 
> "\hspace{3mm}Interest in War News Index" honolulu_contact "\hspace{3mm}Local Contact Index") nolines prehead(\begin{tabular}{l*{5}{
> c}} \hline & \\) posthead(\hline) prefoot() postfoot(& \\ \hline \end{tabular}) extracols(1 2) collabels(" " " " " ") mti("Not Expo
> sed to Gas" "Exposed to Gas" "Difference-in-Means (Not-Exposed - Exposed)") nonum noobs replace
(output written to ~/Desktop/JOP Replication/Results/CW_DiffMeans2.tex)

. 
. drop school1-rankgrade6

. 
. ********************************************************************************
. *                                                                       MAIN ANALYSES                                              
>         *
. ********************************************************************************
. 
. eststo clear

.         
. eststo: reghdfe use numchamber nummask, cluster(base) noabs
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =        634
Absorbing 1 HDFE group                            F(   2,      4) =       4.23
Statistics robust to heteroskedasticity           Prob > F        =     0.1030
                                                  R-squared       =     0.0052
                                                  Adj R-squared   =     0.0021
                                                  Within R-sq.    =     0.0052
Number of clusters (base)    =          5         Root MSE        =     0.2904

                                   (Std. Err. adjusted for 5 clusters in base)
------------------------------------------------------------------------------
             |               Robust
         use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  numchamber |  -.0182733   .0084744    -2.16   0.097    -.0418021    .0052555
     nummask |   .0065789   .0055218     1.19   0.299    -.0087522      .02191
       _cons |   .9150647   .0086593   105.67   0.000     .8910226    .9391067
------------------------------------------------------------------------------
(est1 stored)

.         estadd local Cluster "Yes"

added macro:
            e(Cluster) : "Yes"

. 
. eststo: reghdfe use numchamber nummask, cluster(base) abs($demographic)
(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =        634
Absorbing 4 HDFE groups                           F(   2,      4) =       8.70
Statistics robust to heteroskedasticity           Prob > F        =     0.0349
                                                  R-squared       =     0.0490
                                                  Adj R-squared   =    -0.0016
                                                  Within R-sq.    =     0.0038
Number of clusters (base)    =          5         Root MSE        =     0.2910

                                   (Std. Err. adjusted for 5 clusters in base)
------------------------------------------------------------------------------
             |               Robust
         use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  numchamber |  -.0179683   .0057433    -3.13   0.035    -.0339142   -.0020223
     nummask |   .0041956   .0067496     0.62   0.568    -.0145442    .0229354
       _cons |   .9196014   .0170384    53.97   0.000     .8722954    .9669075
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est2 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local Cluster "Yes"

added macro:
            e(Cluster) : "Yes"

. 
. eststo: reghdfe use numchamber nummask ally_relations, cluster(base) abs($demographic)
(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =        634
Absorbing 4 HDFE groups                           F(   3,      4) =     237.88
Statistics robust to heteroskedasticity           Prob > F        =     0.0001
                                                  R-squared       =     0.0676
                                                  Adj R-squared   =     0.0163
                                                  Within R-sq.    =     0.0233
Number of clusters (base)    =          5         Root MSE        =     0.2884

                                     (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------
               |               Robust
           use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
    numchamber |  -.0167449   .0043185    -3.88   0.018    -.0287349   -.0047549
       nummask |   .0042546   .0067591     0.63   0.563    -.0145116    .0230208
ally_relations |   .0417853   .0065291     6.40   0.003     .0236577    .0599129
         _cons |   .9182988    .018552    49.50   0.000     .8667901    .9698074
--------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est3 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Cluster "Yes"

added macro:
            e(Cluster) : "Yes"

. 
. eststo: reghdfe use numchamber nummask ally_relations orientation_officers, cluster(base) abs($demographic)
(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =        634
Absorbing 4 HDFE groups                           F(   4,      4) =    1101.08
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.0690
                                                  Adj R-squared   =     0.0161
                                                  Within R-sq.    =     0.0247
Number of clusters (base)    =          5         Root MSE        =     0.2884

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0141823   .0057689    -2.46   0.070    -.0301993    .0018347
             nummask |   .0052514   .0063014     0.83   0.452     -.012244    .0227469
      ally_relations |   .0429339   .0064587     6.65   0.003     .0250018     .060866
orientation_officers |  -.0115292   .0106481    -1.08   0.340     -.041093    .0180346
               _cons |   .9133833   .0179466    50.89   0.000     .8635556    .9632111
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est4 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Cluster "Yes"

added macro:
            e(Cluster) : "Yes"

. 
. eststo: reghdfe use numchamber nummask ally_relations orientation_officers infocenter orient_meet war_interest, cluster(base) abs($
> demographic)
(MWFE estimator converged in 8 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        634
Absorbing 4 HDFE groups                           F(   7,      4) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.0803
                                                  Adj R-squared   =     0.0233
                                                  Within R-sq.    =     0.0367
Number of clusters (base)    =          5         Root MSE        =     0.2873

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0267503   .0077795    -3.44   0.026    -.0483497   -.0051509
             nummask |   .0037309    .005707     0.65   0.549    -.0121144    .0195762
      ally_relations |   .0432873   .0070813     6.11   0.004     .0236264    .0629481
orientation_officers |  -.0198418    .014698    -1.35   0.248      -.06065    .0209665
          infocenter |   .0177041   .0294788     0.60   0.580    -.0641421    .0995503
         orient_meet |   .0643384   .0294318     2.19   0.094    -.0173773    .1460541
        war_interest |   .0046164   .0163497     0.28   0.792    -.0407777    .0500105
               _cons |   .8865685    .017843    49.69   0.000     .8370283    .9361086
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est5 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Cluster "Yes"

added macro:
            e(Cluster) : "Yes"

. 
. eststo: reghdfe use numchamber nummask ally_relations orientation_officers infocenter orient_meet war_interest honolulu_contact, cl
> uster(base) abs($demographic)
(MWFE estimator converged in 8 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        634
Absorbing 4 HDFE groups                           F(   8,      4) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.0815
                                                  Adj R-squared   =     0.0229
                                                  Within R-sq.    =     0.0379
Number of clusters (base)    =          5         Root MSE        =     0.2874

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0264483   .0074404    -3.55   0.024    -.0471062   -.0057903
             nummask |   .0041609   .0055536     0.75   0.495    -.0112583    .0195802
      ally_relations |   .0424534   .0077925     5.45   0.006     .0208179    .0640889
orientation_officers |  -.0206984    .015128    -1.37   0.243    -.0627005    .0213038
          infocenter |   .0163578   .0290808     0.56   0.604    -.0643835    .0970992
         orient_meet |   .0650294   .0289706     2.24   0.088    -.0154058    .1454645
        war_interest |   .0040719   .0166763     0.24   0.819     -.042229    .0503728
    honolulu_contact |   .0105379   .0164545     0.64   0.557    -.0351471    .0562229
               _cons |   .8857784    .017579    50.39   0.000     .8369712    .9345857
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est6 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Local "Yes"

added macro:
              e(Local) : "Yes"

.         estadd local Cluster "Yes"

added macro:
            e(Cluster) : "Yes"

. 
. eststo: reghdfe use numchamber nummask ally_relations orientation_officers infocenter orient_meet war_interest honolulu_contact, cl
> uster(base) abs($demographic base)
(MWFE estimator converged in 9 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        634
Absorbing 5 HDFE groups                           F(   8,      4) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.0840
                                                  Adj R-squared   =     0.0172
                                                  Within R-sq.    =     0.0367
Number of clusters (base)    =          5         Root MSE        =     0.2882

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |   -.026248   .0068786    -3.82   0.019     -.045346     -.00715
             nummask |   .0024635   .0064859     0.38   0.723    -.0155442    .0204713
      ally_relations |   .0430913   .0080686     5.34   0.006     .0206894    .0654932
orientation_officers |  -.0228486   .0167005    -1.37   0.243    -.0692167    .0235195
          infocenter |   .0110018   .0325469     0.34   0.752    -.0793629    .1013665
         orient_meet |   .0616907   .0346553     1.78   0.150    -.0345279    .1579093
        war_interest |   .0033358   .0166075     0.20   0.851     -.042774    .0494456
    honolulu_contact |   .0103066    .016226     0.64   0.560     -.034744    .0553572
               _cons |   .8938628   .0179752    49.73   0.000     .8439557    .9437698
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
          base |         5           5           0    *|
-------------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation
(est7 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Local "Yes"

added macro:
              e(Local) : "Yes"

.         estadd local Base "Yes"

added macro:
               e(Base) : "Yes"

.         estadd local Cluster "Yes"

added macro:
            e(Cluster) : "Yes"

. 
. eststo: reghdfe use numchamber nummask ally_relations orientation_officers infocenter orient_meet war_interest honolulu_contact, vc
> e(robust) abs($demographic base)
(MWFE estimator converged in 9 iterations)

HDFE Linear regression                            Number of obs   =        634
Absorbing 5 HDFE groups                           F(   8,    591) =       3.37
                                                  Prob > F        =     0.0009
                                                  R-squared       =     0.0840
                                                  Adj R-squared   =     0.0189
                                                  Within R-sq.    =     0.0367
                                                  Root MSE        =     0.2880

--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |   -.026248   .0149322    -1.76   0.079    -.0555746    .0030786
             nummask |   .0024635   .0070671     0.35   0.728    -.0114161    .0163432
      ally_relations |   .0430913   .0096598     4.46   0.000     .0241195    .0620631
orientation_officers |  -.0228486   .0129632    -1.76   0.078    -.0483082     .002611
          infocenter |   .0110018   .0283439     0.39   0.698    -.0446653    .0666689
         orient_meet |   .0616907   .0300783     2.05   0.041     .0026173     .120764
        war_interest |   .0033358   .0122961     0.27   0.786    -.0208136    .0274852
    honolulu_contact |   .0103066   .0118893     0.87   0.386    -.0130439     .033657
               _cons |   .8938628   .0287752    31.06   0.000     .8373487    .9503769
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
          base |         5           1           4    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est8 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Local "Yes"

added macro:
              e(Local) : "Yes"

.         estadd local Base "Yes"

added macro:
               e(Base) : "Yes"

.         estadd local Cluster "No"

added macro:
            e(Cluster) : "No"

.         
. esttab using "${result}/CW_Main.tex", style(tex) b(3) se(3) nonotes keep(numchamber nummask) stats(N aic cov Cluster Demog ForRel O
> fficer Info Local Base, fmt(0 0 3 3) labels("Observations" "AIC" "\hline &\\ \textsc{Parameters}" "\hspace{3mm}Airbase Clustered SE
> s" "\hspace{3mm}Demographics" "\hspace{3mm}Postwar Foreign Policy" "\hspace{3mm}Officers' Leadership" "\hspace{3mm}Information Acce
> ss" "\hspace{3mm}Local Contact" "\hspace{3mm}Airbase FE")) varlabels(numchamber "& \\ Gas Exposure" nummask "& \\ Gas Mask Training
> ") starlevels(* .10 ** .05 *** .01) nolines prehead(\begin{tabular}{l*{17}{c}} \hline & \\ & & \multicolumn{15}{c}{\textbf{Support 
> for Using Chemical Weapons Against Japan (=1)}} \\ \cline{3-17} & \\) posthead(\hline) prefoot() postfoot(& \\ \hline \end{tabular}
> ) extracols(1 2 3 4 5 6 7 8) nomtit replace
(output written to ~/Desktop/JOP Replication/Results/CW_Main.tex)

. 
. eststo clear

. 
. ********************************************************************************
. *                                                                       OSTERS BOUNDS                                              
>         *
. ********************************************************************************
. 
. eststo clear

.         
. qui reg use numchamber nummask, cluster(base)

. psacalc delta numchamber, rmax(.00676)

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |       9.58217
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |       -0.01896                   0.004
Controlled   |       -0.01827                   0.005
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.007
Beta         |    0.000000
Unr. Controls|   
-------------+----------------------------------------------------------------

. 
. qui reg use numchamber nummask i.($demographic), cluster(base)

. psacalc delta numchamber, rmax(.0637)

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |      15.44689
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |       -0.01896                   0.004
Controlled   |       -0.01797                   0.049
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.064
Beta         |    0.000000
Unr. Controls|   
-------------+----------------------------------------------------------------

. 
. qui reg use numchamber nummask i.($demographic) ally_relations, cluster(base)

. psacalc delta numchamber, rmax(.08788)

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |      11.13302
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |       -0.01896                   0.004
Controlled   |       -0.01674                   0.068
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.088
Beta         |    0.000000
Unr. Controls|   
-------------+----------------------------------------------------------------

. 
. qui reg use numchamber nummask i.($demographic) ally_relations orientation_officers, cluster(base)

. psacalc delta numchamber, rmax(.0897)

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |       5.47635
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |       -0.01896                   0.004
Controlled   |       -0.01418                   0.069
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.090
Beta         |    0.000000
Unr. Controls|   
-------------+----------------------------------------------------------------

. 
. qui reg use numchamber nummask i.($demographic) ally_relations orientation_officers infocenter orient_meet war_interest, cluster(ba
> se)

. psacalc delta numchamber, rmax(.10439)

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |     -34.27052
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |       -0.01896                   0.004
Controlled   |       -0.02675                   0.080
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.104
Beta         |    0.000000
Unr. Controls|   
-------------+----------------------------------------------------------------

. 
. qui reg use numchamber nummask i.($demographic) ally_relations orientation_officers infocenter orient_meet war_interest honolulu_co
> ntact, cluster(base)

. psacalc delta numchamber, rmax(.10595)

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |     -40.65427
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |       -0.01896                   0.004
Controlled   |       -0.02645                   0.082
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.106
Beta         |    0.000000
Unr. Controls|   
-------------+----------------------------------------------------------------

. 
. qui reg use numchamber nummask i.($demographic base) ally_relations orientation_officers infocenter orient_meet war_interest honolu
> lu_contact, cluster(base)

. psacalc delta numchamber, rmax(.1092)

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |    -215.02592
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |       -0.01896                   0.004
Controlled   |       -0.02625                   0.084
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.109
Beta         |    0.000000
Unr. Controls|   
-------------+----------------------------------------------------------------

. 
. qui reg use numchamber nummask i.($demographic base) ally_relations orientation_officers infocenter orient_meet war_interest honolu
> lu_contact, vce(robust)

. psacalc delta numchamber, rmax(.084)

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |   -1288.24826
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |       -0.01896                   0.004
Controlled   |       -0.02625                   0.084
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.084
Beta         |    0.000000
Unr. Controls|   
-------------+----------------------------------------------------------------

. 
. eststo clear

. 
. ********************************************************************************
. *                                                               WILD CLUSTER BOOTSTRAP                                     *
. ********************************************************************************
. 
. eststo clear

.         
. eststo: qui reg use numchamber nummask, cluster(base)
(est1 stored)

. boottest numchamber=0, boot(wild) weight(webb) nograph seed(8675309) level(90)

Wild bootstrap-t, null imposed, 999 replications, Wald test, bootstrap clustering by base, Webb weights:
  numchamber=0

                            t(4) =    -2.1563
                        Prob>|t| =     0.1091

90% confidence set for null hypothesis expression: [−.03311, .0009235]

. boottest nummask=0, boot(wild) weight(webb) nograph seed(8675309) level(90)

Wild bootstrap-t, null imposed, 999 replications, Wald test, bootstrap clustering by base, Webb weights:
  nummask=0

                            t(4) =     1.1914
                        Prob>|t| =     0.2863

90% confidence set for null hypothesis expression: [−.00792, .01596]

. 
. eststo: qui reg use numchamber nummask i.($demographic), cluster(base)
(est2 stored)

.         estadd local Demog "Yes" 

added macro:
              e(Demog) : "Yes"

. boottest numchamber=0, boot(wild) weight(webb) nograph seed(8675309) level(90)

Wild bootstrap-t, null imposed, 999 replications, Wald test, bootstrap clustering by base, Webb weights:
  numchamber=0

                            t(4) =    -3.1286
                        Prob>|t| =     0.0861

90% confidence set for null hypothesis expression: [−.02645, −.004031]

. boottest nummask=0, boot(wild) weight(webb) nograph seed(8675309) level(90)

Wild bootstrap-t, null imposed, 999 replications, Wald test, bootstrap clustering by base, Webb weights:
  nummask=0

                            t(4) =     0.6216
                        Prob>|t| =     0.5586

90% confidence set for null hypothesis expression: [−.01438, .01541]

. 
. eststo: qui reg use numchamber nummask ally_relations i.($demographic), cluster(base)
(est3 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

. boottest numchamber=0, boot(wild) weight(webb) nograph seed(8675309) level(90)

Wild bootstrap-t, null imposed, 999 replications, Wald test, bootstrap clustering by base, Webb weights:
  numchamber=0

                            t(4) =    -3.8775
                        Prob>|t| =     0.0771

90% confidence set for null hypothesis expression: [−.02249, −.005321]

. boottest nummask=0, boot(wild) weight(webb) nograph seed(8675309) level(90)

Wild bootstrap-t, null imposed, 999 replications, Wald test, bootstrap clustering by base, Webb weights:
  nummask=0

                            t(4) =     0.6295
                        Prob>|t| =     0.5616

90% confidence set for null hypothesis expression: [−.01502, .01496]

. 
. eststo: qui reg use numchamber nummask ally_relations orientation_officers i.($demographic), cluster(base)
(est4 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

. boottest numchamber=0, boot(wild) weight(webb) nograph seed(8675309) level(90)

Wild bootstrap-t, null imposed, 999 replications, Wald test, bootstrap clustering by base, Webb weights:
  numchamber=0

                            t(4) =    -2.4584
                        Prob>|t| =     0.0881

90% confidence set for null hypothesis expression: [−.02272, −.0007698]

. boottest nummask=0, boot(wild) weight(webb) nograph seed(8675309) level(90)

Wild bootstrap-t, null imposed, 999 replications, Wald test, bootstrap clustering by base, Webb weights:
  nummask=0

                            t(4) =     0.8334
                        Prob>|t| =     0.4835

90% confidence set for null hypothesis expression: [−.01182, .01524]

. 
. eststo: qui reg use numchamber nummask ally_relations orientation_officers infocenter orient_meet war_interest i.($demographic), cl
> uster(base)
(est5 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

. boottest numchamber=0, boot(wild) weight(webb) nograph seed(8675309) level(90)

Wild bootstrap-t, null imposed, 999 replications, Wald test, bootstrap clustering by base, Webb weights:
  numchamber=0

                            t(4) =    -3.4386
                        Prob>|t| =     0.0130

90% confidence set for null hypothesis expression: [−.03994, −.008618]

. boottest nummask=0, boot(wild) weight(webb) nograph seed(8675309) level(90)

Wild bootstrap-t, null imposed, 999 replications, Wald test, bootstrap clustering by base, Webb weights:
  nummask=0

                            t(4) =     0.6537
                        Prob>|t| =     0.5235

90% confidence set for null hypothesis expression: [−.01681, .0128]

. 
. eststo: qui reg use numchamber nummask ally_relations orientation_officers infocenter orient_meet war_interest honolulu_contact i.(
> $demographic), cluster(base)
(est6 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Local "Yes"

added macro:
              e(Local) : "Yes"

. boottest numchamber=0, boot(wild) weight(webb) nograph seed(8675309) level(90)

Wild bootstrap-t, null imposed, 999 replications, Wald test, bootstrap clustering by base, Webb weights:
  numchamber=0

                            t(4) =    -3.5547
                        Prob>|t| =     0.0200

90% confidence set for null hypothesis expression: [−.04144, −.009091]

. boottest nummask=0, boot(wild) weight(webb) nograph seed(8675309) level(90)

Wild bootstrap-t, null imposed, 999 replications, Wald test, bootstrap clustering by base, Webb weights:
  nummask=0

                            t(4) =     0.7492
                        Prob>|t| =     0.4625

90% confidence set for null hypothesis expression: [−.01611, .01299]

. 
. eststo: qui reghdfe use numchamber nummask ally_relations orientation_officers infocenter orient_meet war_interest honolulu_contact
>  i.($demographic), cluster(base) abs(base)
(est7 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Local "Yes"

added macro:
              e(Local) : "Yes"

.         estadd local Base "Yes"

added macro:
               e(Base) : "Yes"

. boottest numchamber=0, boot(wild) weight(webb) nograph seed(8675309) level(90)

Wild bootstrap-t, null imposed, 999 replications, Wald test, bootstrap clustering by base, Webb weights:
  numchamber=0

                            t(4) =    -3.8159
                        Prob>|t| =     0.0180

90% confidence set for null hypothesis expression: [−.04229, −.01312]

. boottest nummask=0, boot(wild) weight(webb) nograph seed(8675309) level(90)

Wild bootstrap-t, null imposed, 999 replications, Wald test, bootstrap clustering by base, Webb weights:
  nummask=0

                            t(4) =     0.3798
                        Prob>|t| =     0.6827

90% confidence set for null hypothesis expression: [−.02229, .01252]

. 
. esttab using "${result}/CW_Wild.tex", style(tex) b(3) se(3) nonotes keep(numchamber nummask) stats(N aic cov Demog ForRel Officer I
> nfo Local Base, fmt(0 0 3 3) labels("Observations" "AIC" "\hline &\\ \textsc{Parameters}" "\hspace{3mm}Demographics" "\hspace{3mm}P
> ostwar Foreign Policy" "\hspace{3mm}Officers' Leadership" "\hspace{3mm}Information Access" "\hspace{3mm}Local Contact" "\hspace{3mm
> }Airbase FE")) varlabels(numchamber "& \\ Gas Exposure" nummask "& \\ Gas Mask Training") starlevels(* .10 ** .05 *** .01) nolines 
> prehead(\begin{tabular}{l*{15}{c}} \hline & \\ & & \multicolumn{13}{c}{\textbf{Support for Using Chemical Weapons Against Japan (=1
> )}} \\ \cline{3-15} & \\) posthead(\hline) prefoot() postfoot(& \\ \hline \end{tabular}) extracols(1 2 3 4 5 6 7) nomtit replace
(tabulating estimates stored by eststo; specify "." to tabulate the active results)
(output written to ~/Desktop/JOP Replication/Results/CW_Wild.tex)

. 
. eststo clear

. 
. ********************************************************************************
. *                                                               MULTINOMIAL LOGIT                                                  
>         *
. ********************************************************************************
. 
. eststo clear

. 
. eststo: mlogit use3 numchamber nummask, cluster(base) r base(0) rrr

Iteration 0:   log pseudolikelihood = -526.29114  
Iteration 1:   log pseudolikelihood = -523.85499  
Iteration 2:   log pseudolikelihood = -523.83885  
Iteration 3:   log pseudolikelihood = -523.83885  

Multinomial logistic regression                 Number of obs     =        634
                                                Wald chi2(2)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -523.83885               Pseudo R2         =     0.0047

                                   (Std. Err. adjusted for 5 clusters in base)
------------------------------------------------------------------------------
             |               Robust
        use3 |        RRR   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0            |  (base outcome)
-------------+----------------------------------------------------------------
1            |
  numchamber |   .7848169   .0600768    -3.17   0.002     .6754759    .9118572
     nummask |   1.072977    .072068     1.05   0.294     .9406287    1.223947
       _cons |   8.502912   1.090044    16.70   0.000     6.613732    10.93173
-------------+----------------------------------------------------------------
2            |
  numchamber |   .8855407   .0918173    -1.17   0.241     .7226894    1.085089
     nummask |   1.107602   .0720394     1.57   0.116     .9750359    1.258191
       _cons |   2.431475   .0651146    33.18   0.000     2.307144    2.562506
------------------------------------------------------------------------------
Note: _cons estimates baseline relative risk for each outcome.
(est1 stored)

. 
. eststo: mlogit use3 numchamber nummask 0.age 1.age 2.age 3.age 4.age 5.age 6.age 7.age 8.age 9.age 10.age 1.monthsoversea 2.monthso
> versea 3.monthsoversea 4.monthsoversea 5.monthsoversea 6.monthsoversea 7.monthsoversea 8.monthsoversea 9.monthsoversea i.(school ra
> nkgrade), cluster(base) base(0) rrr

Iteration 0:   log pseudolikelihood = -526.29114  
Iteration 1:   log pseudolikelihood = -497.96567  
Iteration 2:   log pseudolikelihood = -495.24637  
Iteration 3:   log pseudolikelihood =  -495.0271  
Iteration 4:   log pseudolikelihood = -494.98805  
Iteration 5:   log pseudolikelihood = -494.97982  
Iteration 6:   log pseudolikelihood = -494.97782  
Iteration 7:   log pseudolikelihood =  -494.9774  
Iteration 8:   log pseudolikelihood = -494.97731  
Iteration 9:   log pseudolikelihood = -494.97729  

Multinomial logistic regression                 Number of obs     =        634
                                                Wald chi2(4)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -494.97729               Pseudo R2         =     0.0595

                                        (Std. Err. adjusted for 5 clusters in base)
-----------------------------------------------------------------------------------
                  |               Robust
             use3 |        RRR   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
0                 |  (base outcome)
------------------+----------------------------------------------------------------
1                 |
       numchamber |   .7739691   .0460806    -4.30   0.000     .6887233     .869766
          nummask |   1.044852   .0832901     0.55   0.582     .8937201    1.221541
                  |
              age |
      18 or Less  |   525454.8   486283.6    14.23   0.000     85662.06     3223163
              19  |   .1905227   .2374284    -1.33   0.183     .0165647    2.191341
              20  |    1.46103    .998813     0.55   0.579     .3826017    5.579192
              21  |   .9942231    .508306    -0.01   0.991     .3650051    2.708125
              22  |   .9805312   .6060633    -0.03   0.975     .2919688     3.29296
              23  |   .6250048   .4460718    -0.66   0.510     .1543025    2.531593
              24  |   1.847124   1.051675     1.08   0.281     .6051423    5.638125
              25  |   .8537004   .3396565    -0.40   0.691     .3914202    1.861949
           26-29  |   2.182874   .9957805     1.71   0.087     .8927479    5.337386
           30-34  |    2.42266   .8329024     2.57   0.010     1.234958    4.752616
                  |
    monthsoversea |
      3-6 Months  |   .6231429   .3438751    -0.86   0.391     .2112811    1.837869
      6-9 Months  |   3.002114   2.317812     1.42   0.154     .6610716    13.63345
     9-12 Months  |   1.342477   .5095475     0.78   0.438     .6380088    2.824795
    12-18 Months  |   .7684721   .4403752    -0.46   0.646     .2499451    2.362717
    18-24 Months  |   1.882564   1.517125     0.79   0.432     .3879552    9.135198
    24-30 Months  |    .805127   .4899576    -0.36   0.722     .2442689    2.653753
    30-36 Months  |   1.052279    1.19701     0.04   0.964     .1132022    9.781535
      36+ Months  |   1.384818    .933798     0.48   0.629     .3693333    5.192389
                  |
           school |
      <8th Grade  |   6.805737    4.79625     2.72   0.007     1.710025    27.08618
       8th Grade  |   5.683965   8.080149     1.22   0.222     .3504368    92.19198
Some High School  |   6.923053   7.968701     1.68   0.093     .7253237      66.079
     High School  |   10.39719   9.046175     2.69   0.007     1.889383    57.21528
         College  |   4.109161   4.484224     1.30   0.195     .4840143    34.88575
                  |
        rankgrade |
    PRV. or PFC.  |   1.549509   2.478231     0.27   0.784     .0674237    35.61034
     CPL or TCH5  |   1.748358   3.452215     0.28   0.777     .0364664    83.82382
     SGT or TCH4  |   1.036011   1.784198     0.02   0.984     .0354358    30.28914
    SSGT or TCH3  |   1.600303    2.26671     0.33   0.740     .0996641    25.69601
TSGT, MSGT, 1SGT  |   .8415007   1.267181    -0.11   0.909      .043981    16.10065
                  |
            _cons |   .6757801   .7612419    -0.35   0.728     .0742947    6.146858
------------------+----------------------------------------------------------------
2                 |
       numchamber |   .8839869   .0435504    -2.50   0.012     .8026213     .973601
          nummask |    1.07945   .0791086     1.04   0.297     .9350205    1.246188
                  |
              age |
      18 or Less  |   .5699508   .4625084    -0.69   0.488     .1161709    2.796259
              19  |   .4410844   .4049679    -0.89   0.373     .0729487    2.667018
              20  |   1.551033   .2857456     2.38   0.017     1.080948    2.225549
              21  |   1.057517   .5089557     0.12   0.907     .4117425    2.716119
              22  |   .4138212   .3225895    -1.13   0.258     .0897984    1.907027
              23  |    .780995   .5863914    -0.33   0.742      .179285    3.402143
              24  |   2.399164   1.993632     1.05   0.292     .4706925    12.22876
              25  |   .7006988   .3197448    -0.78   0.436     .2864895    1.713776
           26-29  |   2.500516   1.549714     1.48   0.139      .742149    8.424967
           30-34  |   2.291086   .9782643     1.94   0.052     .9921774    5.290461
                  |
    monthsoversea |
      3-6 Months  |   .2830961   .1795911    -1.99   0.047     .0816482    .9815694
      6-9 Months  |   2.048123   1.651308     0.89   0.374     .4217657     9.94583
     9-12 Months  |    .963605   .5483135    -0.07   0.948     .3158971    2.939358
    12-18 Months  |   .3412275   .2838515    -1.29   0.196     .0668295    1.742286
    18-24 Months  |   1.340012   1.416502     0.28   0.782     .1687794    10.63893
    24-30 Months  |   .3495146   .2565971    -1.43   0.152     .0828998    1.473592
    30-36 Months  |   .7869899    .413211    -0.46   0.648     .2812183    2.202392
      36+ Months  |   .8940789   .7904442    -0.13   0.899     .1580662    5.057229
                  |
           school |
      <8th Grade  |    1.05838    .875051     0.07   0.945     .2093545     5.35058
       8th Grade  |   .7486606   .2339609    -0.93   0.354     .4057696    1.381308
Some High School  |   1.353211   .5701074     0.72   0.473     .5925903    3.090128
     High School  |   1.516631    .768374     0.82   0.411     .5618675    4.093794
         College  |   .5062685    .268814    -1.28   0.200     .1788205    1.433324
                  |
        rankgrade |
    PRV. or PFC.  |    2131079    2194219    14.15   0.000     283255.5    1.60e+07
     CPL or TCH5  |    1723338    1987850    12.45   0.000     179688.2    1.65e+07
     SGT or TCH4  |    1312109    1615473    11.44   0.000       117481    1.47e+07
    SSGT or TCH3  |    1559742    1191891    18.66   0.000     348818.3     6974388
TSGT, MSGT, 1SGT  |   855115.5   988503.9    11.82   0.000     88724.86     8241461
                  |
            _cons |   1.68e-06   2.20e-06   -10.18   0.000     1.30e-07    .0000217
-----------------------------------------------------------------------------------
Note: _cons estimates baseline relative risk for each outcome.
Note: 1 observation completely determined.  Standard errors questionable.
(est2 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

. 
. eststo: mlogit use3 numchamber nummask 0.age 1.age 2.age 3.age 4.age 5.age 6.age 7.age 8.age 9.age 10.age 1.monthsoversea 2.monthso
> versea 3.monthsoversea 4.monthsoversea 5.monthsoversea 6.monthsoversea 7.monthsoversea 8.monthsoversea 9.monthsoversea i.(school ra
> nkgrade) ally_relations, cluster(base) base(0) rrr

Iteration 0:   log pseudolikelihood = -526.29114  
Iteration 1:   log pseudolikelihood = -491.99678  
Iteration 2:   log pseudolikelihood = -487.47763  
Iteration 3:   log pseudolikelihood = -487.13965  
Iteration 4:   log pseudolikelihood = -487.07318  
Iteration 5:   log pseudolikelihood =  -487.0578  
Iteration 6:   log pseudolikelihood = -487.05439  
Iteration 7:   log pseudolikelihood = -487.05364  
Iteration 8:   log pseudolikelihood = -487.05352  
Iteration 9:   log pseudolikelihood =  -487.0535  

Multinomial logistic regression                 Number of obs     =        634
                                                Wald chi2(5)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood =  -487.0535               Pseudo R2         =     0.0746

                                        (Std. Err. adjusted for 5 clusters in base)
-----------------------------------------------------------------------------------
                  |               Robust
             use3 |        RRR   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
0                 |  (base outcome)
------------------+----------------------------------------------------------------
1                 |
       numchamber |   .7824804   .0395103    -4.86   0.000     .7087502    .8638806
          nummask |   1.046237   .0911501     0.52   0.604     .8820062    1.241047
                  |
              age |
      18 or Less  |    1172891    1231456    13.31   0.000     149814.2     9182523
              19  |   .1963614    .225408    -1.42   0.156     .0206986    1.862819
              20  |   1.338491   .8538766     0.46   0.648     .3833538    4.673377
              21  |   .8180612   .4127417    -0.40   0.691     .3043151    2.199116
              22  |   .9641366   .6301913    -0.06   0.955     .2677721    3.471457
              23  |   .6757628   .5040363    -0.53   0.599     .1566398    2.915321
              24  |   1.997568   1.051732     1.31   0.189     .7117684    5.606144
              25  |   .8366625   .3349378    -0.45   0.656     .3817616    1.833616
           26-29  |   2.092674   .9533265     1.62   0.105     .8569057    5.110578
           30-34  |   2.302521   .9277435     2.07   0.038     1.045281    5.071939
                  |
    monthsoversea |
      3-6 Months  |   .6076754   .3418057    -0.89   0.376     .2017842    1.830021
      6-9 Months  |    3.60143   2.170599     2.13   0.034     1.105227    11.73541
     9-12 Months  |   1.564882   .6023575     1.16   0.245      .735928    3.327575
    12-18 Months  |   .7746269   .4466495    -0.44   0.658     .2502017    2.398253
    18-24 Months  |    1.80557   1.373156     0.78   0.437     .4066922    8.016091
    24-30 Months  |   .8789994   .5428367    -0.21   0.835     .2620105    2.948889
    30-36 Months  |   1.007238   1.240548     0.01   0.995     .0901081    11.25902
      36+ Months  |   1.577629   1.085881     0.66   0.508     .4093771    6.079757
                  |
           school |
      <8th Grade  |   15.54458   7.307856     5.84   0.000     6.186006    39.06141
       8th Grade  |   14.93153   14.71182     2.74   0.006     2.164852    102.9866
Some High School  |   21.78112   17.78714     3.77   0.000     4.395038    107.9438
     High School  |   31.44966   19.11109     5.67   0.000      9.55794    103.4827
         College  |   12.79864    11.7321     2.78   0.005      2.12273    77.16723
                  |
        rankgrade |
    PRV. or PFC.  |   2.132536   2.454664     0.66   0.511     .2234176     20.3552
     CPL or TCH5  |   2.447933    3.77855     0.58   0.562     .1188263    50.42971
     SGT or TCH4  |   1.280893   1.589295     0.20   0.842     .1125556    14.57669
    SSGT or TCH3  |    2.39017   2.307866     0.90   0.367      .360195    15.86061
TSGT, MSGT, 1SGT  |   .9449731   1.087734    -0.05   0.961     .0989972    9.020194
                  |
   ally_relations |   2.167332    .304017     5.51   0.000     1.646362    2.853156
            _cons |   .1960472   .2139466    -1.49   0.135      .023091    1.664478
------------------+----------------------------------------------------------------
2                 |
       numchamber |   .8974217   .0574181    -1.69   0.091     .7916545     1.01732
          nummask |   1.080813   .0955229     0.88   0.379     .9089104    1.285229
                  |
              age |
      18 or Less  |   .4757723   .4782668    -0.74   0.460     .0663338    3.412428
              19  |   .4555734   .4164727    -0.86   0.390       .07593    2.733402
              20  |   1.430924   .2947637     1.74   0.082      .955593    2.142694
              21  |   .8730998    .520838    -0.23   0.820     .2711996    2.810857
              22  |   .4103763   .3389079    -1.08   0.281     .0813243    2.070829
              23  |   .8482389   .6744333    -0.21   0.836     .1785374    4.030019
              24  |    2.60621   2.120499     1.18   0.239     .5289852     12.8403
              25  |   .6901999   .3626924    -0.71   0.480     .2464211    1.933178
           26-29  |   2.411427   1.543509     1.38   0.169     .6877584    8.454973
           30-34  |   2.183816   1.055665     1.62   0.106     .8467245    5.632356
                  |
    monthsoversea |
      3-6 Months  |   .2702435   .1841531    -1.92   0.055     .0710751    1.027526
      6-9 Months  |   2.458581   1.607225     1.38   0.169     .6827103    8.853858
     9-12 Months  |    1.12976   .6147136     0.22   0.823     .3889012    3.281961
    12-18 Months  |   .3457796   .2900566    -1.27   0.206     .0667991    1.789897
    18-24 Months  |    1.29176   1.313502     0.25   0.801      .176057    9.477861
    24-30 Months  |   .3818523   .2764983    -1.33   0.184     .0923725    1.578513
    30-36 Months  |   .7751797   .4508361    -0.44   0.661     .2479436     2.42355
      36+ Months  |   1.021479   .9330848     0.02   0.981     .1704855    6.120286
                  |
           school |
      <8th Grade  |    1.12106   1.349456     0.09   0.924     .1059279    11.86444
       8th Grade  |   .9032984   .2245312    -0.41   0.682     .5549446    1.470323
Some High School  |   1.953897   1.015764     1.29   0.198     .7053278    5.412679
     High School  |   2.119426   1.576745     1.01   0.313     .4931371    9.108959
         College  |   .7277504   .4819995    -0.48   0.631     .1987099    2.665296
                  |
        rankgrade |
    PRV. or PFC.  |   1.24e+07   1.55e+07    13.04   0.000      1062487    1.44e+08
     CPL or TCH5  |   1.02e+07   1.35e+07    12.18   0.000       758629    1.37e+08
     SGT or TCH4  |    6848145    9439578    11.42   0.000     459478.2    1.02e+08
    SSGT or TCH3  |    9865449    9986517    15.91   0.000      1356655    7.17e+07
TSGT, MSGT, 1SGT  |    4053153    3728214    16.54   0.000       668085    2.46e+07
                  |
   ally_relations |   2.151605   .2571461     6.41   0.000     1.702285    2.719523
            _cons |   2.47e-07   3.73e-07   -10.09   0.000     1.29e-08    4.75e-06
-----------------------------------------------------------------------------------
Note: _cons estimates baseline relative risk for each outcome.
Note: 2 observations completely determined.  Standard errors questionable.
(est3 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

. 
. eststo: mlogit use3 numchamber nummask 0.age 1.age 2.age 3.age 4.age 5.age 6.age 7.age 8.age 9.age 10.age 1.monthsoversea 2.monthso
> versea 3.monthsoversea 4.monthsoversea 5.monthsoversea 6.monthsoversea 7.monthsoversea 8.monthsoversea 9.monthsoversea i.(school ra
> nkgrade) ally_relations orientation_officers, cluster(base) base(0) rrr

Iteration 0:   log pseudolikelihood = -526.29114  
Iteration 1:   log pseudolikelihood = -491.55113  
Iteration 2:   log pseudolikelihood = -486.90359  
Iteration 3:   log pseudolikelihood = -486.56689  
Iteration 4:   log pseudolikelihood = -486.50158  
Iteration 5:   log pseudolikelihood = -486.48632  
Iteration 6:   log pseudolikelihood = -486.48299  
Iteration 7:   log pseudolikelihood = -486.48224  
Iteration 8:   log pseudolikelihood = -486.48213  
Iteration 9:   log pseudolikelihood = -486.48211  

Multinomial logistic regression                 Number of obs     =        634
                                                Wald chi2(5)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -486.48211               Pseudo R2         =     0.0756

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                use3 |        RRR   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
0                    |  (base outcome)
---------------------+----------------------------------------------------------------
1                    |
          numchamber |   .8100426   .0530506    -3.22   0.001      .712462    .9209881
             nummask |   1.058103   .0868073     0.69   0.491     .9009386    1.242685
                     |
                 age |
         18 or Less  |    1246292    1338314    13.07   0.000     151903.7    1.02e+07
                 19  |   .1962615   .2341398    -1.36   0.172     .0189388    2.033846
                 20  |   1.260955   .8417852     0.35   0.728     .3407661    4.665977
                 21  |   .7749797   .3890307    -0.51   0.612     .2897324    2.072925
                 22  |    .943113     .60742    -0.09   0.928     .2668948    3.332632
                 23  |   .6590095   .4942085    -0.56   0.578     .1515491    2.865696
                 24  |   1.974659   1.023733     1.31   0.189     .7148239    5.454879
                 25  |   .8347453   .3302588    -0.46   0.648      .384401     1.81269
              26-29  |   2.062639   .8685623     1.72   0.086     .9036256    4.708234
              30-34  |   2.282671   .9537777     1.98   0.048     1.006427    5.177312
                     |
       monthsoversea |
         3-6 Months  |   .6857097   .4505674    -0.57   0.566     .1891609    2.485704
         6-9 Months  |   3.492891   2.276674     1.92   0.055     .9735763    12.53142
        9-12 Months  |   1.625272   .6490671     1.22   0.224     .7430038    3.555176
       12-18 Months  |   .7885954   .4601539    -0.41   0.684     .2512864    2.474796
       18-24 Months  |   1.798356   1.349151     0.78   0.434     .4133258    7.824537
       24-30 Months  |   .8752685   .5372733    -0.22   0.828     .2628096    2.915019
       30-36 Months  |   1.000844   1.198811     0.00   0.999     .0956745    10.46975
         36+ Months  |   1.609726   1.192482     0.64   0.520     .3768631    6.875756
                     |
              school |
         <8th Grade  |   19.86407   12.62854     4.70   0.000     5.713718     69.0586
          8th Grade  |   17.91221   19.44279     2.66   0.008     2.134095    150.3435
   Some High School  |    26.5604   25.96663     3.35   0.001     3.908968    180.4709
        High School  |     37.836   29.44114     4.67   0.000     8.233113    173.8787
            College  |   15.54084     16.472     2.59   0.010     1.946579     124.073
                     |
           rankgrade |
       PRV. or PFC.  |   2.072234    2.45661     0.61   0.539     .2029311    21.16064
        CPL or TCH5  |   2.414357   3.800639     0.56   0.576     .1103709    52.81392
        SGT or TCH4  |   1.280409   1.616611     0.20   0.845     .1078062    15.20736
       SSGT or TCH3  |    2.30627   2.318288     0.83   0.406      .321568    16.54045
   TSGT, MSGT, 1SGT  |   .9076607   1.090538    -0.08   0.936      .086143    9.563719
                     |
      ally_relations |   2.215365   .3319278     5.31   0.000     1.651619    2.971534
orientation_officers |   .8624559    .133336    -0.96   0.339     .6370021    1.167705
               _cons |   .1565344   .1840086    -1.58   0.115     .0156318     1.56751
---------------------+----------------------------------------------------------------
2                    |
          numchamber |   .9356365   .0440747    -1.41   0.158     .8531197    1.026135
             nummask |    1.09613    .086805     1.16   0.246     .9385416    1.280179
                     |
                 age |
         18 or Less  |   .5192774   .4851189    -0.70   0.483     .0832129    3.240472
                 19  |   .4538161    .438412    -0.82   0.413     .0683236    3.014316
                 20  |   1.340484   .3318541     1.18   0.237      .825155    2.177647
                 21  |    .822746   .4395428    -0.37   0.715     .2887493    2.344286
                 22  |   .4041132   .3240902    -1.13   0.259     .0839185    1.946026
                 23  |   .8217076   .6480053    -0.25   0.803     .1751672    3.854622
                 24  |   2.585333   2.065811     1.19   0.235     .5399626    12.37853
                 25  |   .6928847   .3755479    -0.68   0.498     .2394994    2.004553
              26-29  |   2.372426   1.455645     1.41   0.159     .7127261    7.897012
              30-34  |   2.175356   1.074041     1.57   0.115     .8265431     5.72526
                     |
       monthsoversea |
         3-6 Months  |   .3123209   .2250098    -1.62   0.106     .0760953    1.281871
         6-9 Months  |   2.365212   1.731454     1.18   0.240     .5633104    9.930984
        9-12 Months  |   1.176873   .6454814     0.30   0.767       .40167    3.448176
       12-18 Months  |   .3527354   .2914361    -1.26   0.207     .0698508    1.781256
       18-24 Months  |   1.281963   1.302967     0.24   0.807     .1748747    9.397746
       24-30 Months  |   .3806142   .2729247    -1.35   0.178      .093351    1.551854
       30-36 Months  |   .7659443   .4115748    -0.50   0.620     .2671837    2.195758
         36+ Months  |   1.042732   1.027661     0.04   0.966     .1511033    7.195674
                     |
              school |
         <8th Grade  |   1.545837   1.735128     0.39   0.698     .1712897    13.95071
          8th Grade  |   1.152675   .1603927     1.02   0.307     .8775331    1.514084
   Some High School  |   2.547108   1.290288     1.85   0.065     .9437443    6.874486
        High School  |   2.716244   1.902902     1.43   0.154      .688094    10.72235
            College  |   .9463789   .5392624    -0.10   0.923     .3097678    2.891304
                     |
           rankgrade |
       PRV. or PFC.  |   1.17e+07   1.51e+07    12.60   0.000     927802.5    1.47e+08
        CPL or TCH5  |    9730688   1.34e+07    11.66   0.000     650351.4    1.46e+08
        SGT or TCH4  |    6679686    9367321    11.21   0.000     427627.4    1.04e+08
       SSGT or TCH3  |    9276516    9321060    15.97   0.000      1294482    6.65e+07
   TSGT, MSGT, 1SGT  |    3798509    3642925    15.80   0.000     579797.3    2.49e+07
                     |
      ally_relations |   2.207665   .2662164     6.57   0.000     1.742967    2.796259
orientation_officers |    .831228   .1154271    -1.33   0.183     .6331688    1.091241
               _cons |   1.87e-07   2.82e-07   -10.30   0.000     9.84e-09    3.57e-06
--------------------------------------------------------------------------------------
Note: _cons estimates baseline relative risk for each outcome.
Note: 2 observations completely determined.  Standard errors questionable.
(est4 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

. 
. eststo: mlogit use3 numchamber nummask 0.age 1.age 2.age 3.age 4.age 5.age 6.age 7.age 8.age 9.age 10.age 1.monthsoversea 2.monthso
> versea 3.monthsoversea 4.monthsoversea 5.monthsoversea 6.monthsoversea 7.monthsoversea 8.monthsoversea 9.monthsoversea i.(school ra
> nkgrade) ally_relations orientation_officers infocenter orient_meet war_interest, cluster(base) base(0) rrr

Iteration 0:   log pseudolikelihood = -526.29114  
Iteration 1:   log pseudolikelihood = -487.72748  
Iteration 2:   log pseudolikelihood =  -482.2914  
Iteration 3:   log pseudolikelihood =  -481.9311  
Iteration 4:   log pseudolikelihood = -481.86521  
Iteration 5:   log pseudolikelihood = -481.84968  
Iteration 6:   log pseudolikelihood = -481.84628  
Iteration 7:   log pseudolikelihood = -481.84553  
Iteration 8:   log pseudolikelihood =  -481.8454  
Iteration 9:   log pseudolikelihood = -481.84539  

Multinomial logistic regression                 Number of obs     =        634
                                                Wald chi2(5)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -481.84539               Pseudo R2         =     0.0845

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                use3 |        RRR   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
0                    |  (base outcome)
---------------------+----------------------------------------------------------------
1                    |
          numchamber |   .6898808   .0351513    -7.29   0.000     .6243139    .7623337
             nummask |   1.031859    .077853     0.42   0.678     .8900162    1.196308
                     |
                 age |
         18 or Less  |   704249.1   566461.9    16.74   0.000     145567.2     3407134
                 19  |    .160059   .1985087    -1.48   0.140     .0140799     1.81953
                 20  |   1.105159    .789608     0.14   0.889      .272435    4.483185
                 21  |   .6361662   .3345481    -0.86   0.390     .2269548    1.783207
                 22  |   .7040014   .4927016    -0.50   0.616     .1785882    2.775201
                 23  |    .558658   .4195314    -0.78   0.438      .128211    2.434259
                 24  |   1.891008    1.06307     1.13   0.257     .6283079    5.691337
                 25  |   .7034224   .2322872    -1.07   0.287      .368238    1.343704
              26-29  |   1.820623   .7080454     1.54   0.123     .8495429    3.901708
              30-34  |   1.898681   .9183757     1.33   0.185     .7357545    4.899715
                     |
       monthsoversea |
         3-6 Months  |   .5268007   .2841387    -1.19   0.235     .1830361    1.516198
         6-9 Months  |   3.219301   2.037946     1.85   0.065     .9309301    11.13284
        9-12 Months  |   1.611209   .5228605     1.47   0.142     .8529523     3.04354
       12-18 Months  |   .7004585    .299047    -0.83   0.404     .3033741    1.617284
       18-24 Months  |   1.727682   1.109085     0.85   0.394     .4909475    6.079845
       24-30 Months  |   .6884584   .3366292    -0.76   0.445     .2640423    1.795072
       30-36 Months  |   .9640791   1.079678    -0.03   0.974      .107361    8.657226
         36+ Months  |   1.385177   .8606856     0.52   0.600     .4098334    4.681694
                     |
              school |
         <8th Grade  |   24.63974   18.10435     4.36   0.000     5.837209     104.008
          8th Grade  |   18.31888   19.90123     2.68   0.007     2.178574    154.0371
   Some High School  |   30.13152   31.56293     3.25   0.001     3.867077     234.779
        High School  |   41.78556   41.30964     3.78   0.000     6.018934    290.0901
            College  |   17.04514   18.78079     2.57   0.010     1.966637    147.7329
                     |
           rankgrade |
       PRV. or PFC.  |   2.314831   2.603611     0.75   0.456     .2553456    20.98505
        CPL or TCH5  |   2.888817   4.458075     0.69   0.492     .1403235    59.47161
        SGT or TCH4  |   1.505897   1.729825     0.36   0.722     .1584964    14.30775
       SSGT or TCH3  |   2.928505   2.907722     1.08   0.279     .4182974     20.5025
   TSGT, MSGT, 1SGT  |   1.305062   1.536026     0.23   0.821     .1299533    13.10614
                     |
      ally_relations |   2.223475   .3777904     4.70   0.000     1.593693    3.102129
orientation_officers |   .7604316   .1593035    -1.31   0.191     .5043606    1.146513
          infocenter |   1.237234   .5048301     0.52   0.602     .5560772    2.752762
         orient_meet |   2.375491   1.209487     1.70   0.089     .8757133     6.44384
        war_interest |   1.113392   .2175164     0.55   0.582     .7591969    1.632834
               _cons |    .114799   .1445423    -1.72   0.086     .0097319    1.354183
---------------------+----------------------------------------------------------------
2                    |
          numchamber |   .8301087    .058822    -2.63   0.009     .7224674    .9537875
             nummask |   1.076679   .0947397     0.84   0.401     .9061224    1.279339
                     |
                 age |
         18 or Less  |   .3365905   .2264126    -1.62   0.105     .0900593    1.257984
                 19  |   .3774205   .4077487    -0.90   0.367     .0454176    3.136365
                 20  |    1.17587   .3582739     0.53   0.595     .6471547    2.136538
                 21  |   .6854566   .3123577    -0.83   0.407     .2806037    1.674428
                 22  |   .3172124    .260989    -1.40   0.163     .0632437    1.591047
                 23  |   .7039006   .5505419    -0.45   0.653     .1519711    3.260331
                 24  |   2.439321   2.087624     1.04   0.297     .4558185    13.05407
                 25  |   .6065579   .2704138    -1.12   0.262     .2531583     1.45329
              26-29  |   2.145289   1.254617     1.31   0.192       .68184     6.74977
              30-34  |   1.847041   .8712555     1.30   0.193     .7327605    4.655766
                     |
       monthsoversea |
         3-6 Months  |   .2632459   .1552198    -2.26   0.024     .0828821     .836108
         6-9 Months  |   2.307649   1.628048     1.19   0.236     .5789572    9.197993
        9-12 Months  |   1.181022   .5459447     0.36   0.719     .4772848    2.922391
       12-18 Months  |    .328179   .2450839    -1.49   0.136     .0759336    1.418363
       18-24 Months  |   1.295201   1.212228     0.28   0.782     .2068547    8.109783
       24-30 Months  |   .3239463   .1963947    -1.86   0.063     .0987247    1.062968
       30-36 Months  |   .7707915   .3724715    -0.54   0.590      .298957    1.987308
         36+ Months  |   .9484827   .8836524    -0.06   0.955     .1527597    5.889114
                     |
              school |
         <8th Grade  |   1.707852   2.260647     0.40   0.686     .1275655     22.8648
          8th Grade  |   1.125994   .4614939     0.29   0.772     .5042744     2.51423
   Some High School  |   2.776594   1.982033     1.43   0.153     .6853211    11.24944
        High School  |   2.823599   2.371943     1.24   0.217     .5441989    14.65036
            College  |   .9827743   .7032844    -0.02   0.981     .2417265    3.995612
                     |
           rankgrade |
       PRV. or PFC.  |   1.25e+07   2.26e+07     9.08   0.000     367179.6    4.27e+08
        CPL or TCH5  |   1.10e+07   2.03e+07     8.82   0.000     300041.1    4.05e+08
        SGT or TCH4  |    7560893   1.37e+07     8.74   0.000     217101.3    2.63e+08
       SSGT or TCH3  |   1.12e+07   1.72e+07    10.54   0.000     545836.6    2.28e+08
   TSGT, MSGT, 1SGT  |    5149518    7350184    10.83   0.000     313906.9    8.45e+07
                     |
      ally_relations |   2.217595   .2558406     6.90   0.000     1.768807    2.780252
orientation_officers |   .7592611   .1416192    -1.48   0.140     .5267724    1.094358
          infocenter |   1.181335   .3922272     0.50   0.616     .6162501    2.264589
         orient_meet |   1.930316   .9044421     1.40   0.160     .7705503    4.835658
        war_interest |    1.00874   .2782865     0.03   0.975     .5874266    1.732227
               _cons |   1.54e-07   2.54e-07    -9.52   0.000     6.10e-09    3.88e-06
--------------------------------------------------------------------------------------
Note: _cons estimates baseline relative risk for each outcome.
Note: 2 observations completely determined.  Standard errors questionable.
(est5 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

. 
. eststo: mlogit use3 numchamber nummask 0.age 1.age 2.age 3.age 4.age 5.age 6.age 7.age 8.age 9.age 10.age 1.monthsoversea 2.monthso
> versea 3.monthsoversea 4.monthsoversea 5.monthsoversea 6.monthsoversea 7.monthsoversea 8.monthsoversea 9.monthsoversea i.(school ra
> nkgrade) ally_relations orientation_officers infocenter orient_meet war_interest honolulu_contact, cluster(base) base(0) rrr

Iteration 0:   log pseudolikelihood = -526.29114  
Iteration 1:   log pseudolikelihood = -483.08846  
Iteration 2:   log pseudolikelihood = -477.48678  
Iteration 3:   log pseudolikelihood = -477.15075  
Iteration 4:   log pseudolikelihood = -477.08816  
Iteration 5:   log pseudolikelihood = -477.07315  
Iteration 6:   log pseudolikelihood = -477.07001  
Iteration 7:   log pseudolikelihood = -477.06934  
Iteration 8:   log pseudolikelihood = -477.06919  
Iteration 9:   log pseudolikelihood = -477.06916  
Iteration 10:  log pseudolikelihood = -477.06915  

Multinomial logistic regression                 Number of obs     =        634
                                                Wald chi2(5)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -477.06915               Pseudo R2         =     0.0935

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                use3 |        RRR   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
0                    |  (base outcome)
---------------------+----------------------------------------------------------------
1                    |
          numchamber |   .6957504   .0303803    -8.31   0.000      .638683    .7579169
             nummask |   1.040383   .0772679     0.53   0.594     .8994469    1.203402
                     |
                 age |
         18 or Less  |   857328.6   704380.7    16.63   0.000       171317     4290363
                 19  |   .1635756   .1917956    -1.54   0.123     .0164312    1.628429
                 20  |    1.19533   .8343784     0.26   0.798     .3043142    4.695194
                 21  |   .6881197   .3604494    -0.71   0.475     .2464844     1.92105
                 22  |   .7693783   .5798846    -0.35   0.728     .1756244    3.370506
                 23  |   .5850722   .4644799    -0.68   0.500     .1234393    2.773099
                 24  |   1.900834   1.033642     1.18   0.238     .6547497    5.518402
                 25  |   .7287603   .2094025    -1.10   0.271     .4149539    1.279881
              26-29  |   1.798315   .5925029     1.78   0.075     .9427896    3.430179
              30-34  |    1.95858   1.010711     1.30   0.193     .7123363    5.385148
                     |
       monthsoversea |
         3-6 Months  |   .5151296    .294935    -1.16   0.247     .1677124    1.582224
         6-9 Months  |   3.221468   1.922887     1.96   0.050     .9999388    10.37849
        9-12 Months  |   1.614354   .5338436     1.45   0.148     .8443426    3.086589
       12-18 Months  |    .680495   .3133018    -0.84   0.403     .2760129    1.677724
       18-24 Months  |   1.786893   1.144339     0.91   0.365      .509311    6.269231
       24-30 Months  |   .6813165   .3463476    -0.75   0.450     .2515591    1.845261
       30-36 Months  |    1.01638   1.145086     0.01   0.988      .111703    9.247988
         36+ Months  |   1.228402    .827843     0.31   0.760     .3278688    4.602365
                     |
              school |
         <8th Grade  |   21.77058   15.87928     4.22   0.000     5.212072    90.93466
          8th Grade  |    17.0789   18.66111     2.60   0.009     2.006327    145.3844
   Some High School  |   27.40977   28.69439     3.16   0.002     3.522172    213.3046
        High School  |   37.60313   36.36316     3.75   0.000     5.650554    250.2401
            College  |   15.30565    16.6747     2.50   0.012     1.809307    129.4766
                     |
           rankgrade |
       PRV. or PFC.  |   2.764534     3.0252     0.93   0.353     .3237137    23.60928
        CPL or TCH5  |   3.559245   5.272523     0.86   0.391     .1951741    64.90733
        SGT or TCH4  |   1.884428   1.983023     0.60   0.547     .2395753    14.82235
       SSGT or TCH3  |   3.716181   3.404384     1.43   0.152     .6170399    22.38105
   TSGT, MSGT, 1SGT  |   1.589494   1.703078     0.43   0.665     .1946393    12.98037
                     |
      ally_relations |    2.17231   .3912411     4.31   0.000      1.52622    3.091906
orientation_officers |   .7465273   .1569909    -1.39   0.165     .4943588    1.127325
          infocenter |   1.220285   .5000469     0.49   0.627      .546584    2.724367
         orient_meet |   2.405393   1.237462     1.71   0.088     .8775721    6.593095
        war_interest |   1.105101   .2207987     0.50   0.617     .7470198    1.634826
    honolulu_contact |   1.217067   .2407906     0.99   0.321     .8258645    1.793579
               _cons |   .0993157   .1233179    -1.86   0.063     .0087117    1.132229
---------------------+----------------------------------------------------------------
2                    |
          numchamber |   .8350264   .0621359    -2.42   0.015     .7217066    .9661391
             nummask |   1.072509   .0949622     0.79   0.429     .9016414    1.275758
                     |
                 age |
         18 or Less  |   .4044897   .1463914    -2.50   0.012     .1989957    .8221882
                 19  |   .3857097   .4282664    -0.86   0.391     .0437666     3.39921
                 20  |   1.163714   .3429213     0.51   0.607     .6531538     2.07337
                 21  |   .6815294   .3640857    -0.72   0.473     .2391976    1.941835
                 22  |   .3059724   .2782118    -1.30   0.193     .0514884    1.818256
                 23  |   .6906099   .5735695    -0.45   0.656     .1356088    3.517045
                 24  |    2.40281   2.203007     0.96   0.339     .3983809     14.4924
                 25  |    .612335   .3238379    -0.93   0.354     .2171825    1.726447
              26-29  |   2.157212   1.364986     1.22   0.224     .6241526    7.455809
              30-34  |   1.833235   .8852405     1.26   0.209     .7115186    4.723349
                     |
       monthsoversea |
         3-6 Months  |   .2797851   .1710551    -2.08   0.037     .0844144    .9273262
         6-9 Months  |   2.303852   1.621138     1.19   0.236     .5800892    9.149859
        9-12 Months  |   1.215165    .577897     0.41   0.682     .4784412    3.086326
       12-18 Months  |   .3370469   .2505595    -1.46   0.143     .0785073    1.447007
       18-24 Months  |   1.305165   1.220407     0.28   0.776     .2088052    8.158113
       24-30 Months  |   .3445752   .2034455    -1.80   0.071     .1083213     1.09611
       30-36 Months  |   .7983822   .3549825    -0.51   0.613     .3339977    1.908439
         36+ Months  |   1.023254   .8932436     0.03   0.979     .1848979    5.662847
                     |
              school |
         <8th Grade  |    1.69487   2.265549     0.39   0.693     .1234032    23.27802
          8th Grade  |   1.079872   .5006235     0.17   0.868     .4352705    2.679077
   Some High School  |   2.686528   2.079771     1.28   0.202     .5891628    12.25032
        High School  |   2.746596   2.466709     1.13   0.261     .4724258     15.9682
            College  |    .998084   .7451718    -0.00   0.998     .2310245    4.311974
                     |
           rankgrade |
       PRV. or PFC.  |   1.58e+07   3.08e+07     8.49   0.000     344067.5    7.23e+08
        CPL or TCH5  |   1.42e+07   2.85e+07     8.19   0.000     275142.3    7.30e+08
        SGT or TCH4  |    9194211   1.78e+07     8.26   0.000     204957.3    4.12e+08
       SSGT or TCH3  |   1.33e+07   2.27e+07     9.63   0.000     472311.8    3.76e+08
   TSGT, MSGT, 1SGT  |    6773050   1.04e+07    10.26   0.000     335732.3    1.37e+08
                     |
      ally_relations |   2.198074   .2291308     7.56   0.000     1.791892     2.69633
orientation_officers |   .7659448   .1506538    -1.36   0.175     .5209242    1.126213
          infocenter |   1.213074   .4291412     0.55   0.585      .606406    2.426673
         orient_meet |   1.913402   .9134457     1.36   0.174     .7506688     4.87713
        war_interest |   1.016135   .2792255     0.06   0.954     .5929914    1.741224
    honolulu_contact |   .8679862   .2462275    -0.50   0.618     .4977906    1.513488
               _cons |   1.18e-07   2.09e-07    -9.01   0.000     3.67e-09    3.79e-06
--------------------------------------------------------------------------------------
Note: _cons estimates baseline relative risk for each outcome.
Note: 2 observations completely determined.  Standard errors questionable.
(est6 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Local "Yes"

added macro:
              e(Local) : "Yes"

. 
. eststo: mlogit use3 numchamber nummask 0.age 1.age 2.age 3.age 4.age 5.age 6.age 7.age 8.age 9.age 10.age 1.monthsoversea 2.monthso
> versea 3.monthsoversea 4.monthsoversea 5.monthsoversea 6.monthsoversea 7.monthsoversea 8.monthsoversea 9.monthsoversea i.(school ra
> nkgrade base) ally_relations orientation_officers infocenter orient_meet war_interest honolulu_contact, cluster(base) base(0) rrr

Iteration 0:   log pseudolikelihood = -526.29114  
Iteration 1:   log pseudolikelihood = -481.26072  
Iteration 2:   log pseudolikelihood =  -475.3829  
Iteration 3:   log pseudolikelihood = -475.02141  
Iteration 4:   log pseudolikelihood =  -474.9555  
Iteration 5:   log pseudolikelihood = -474.94001  
Iteration 6:   log pseudolikelihood = -474.93661  
Iteration 7:   log pseudolikelihood = -474.93586  
Iteration 8:   log pseudolikelihood = -474.93573  
Iteration 9:   log pseudolikelihood = -474.93572  

Multinomial logistic regression                 Number of obs     =        634
                                                Wald chi2(5)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -474.93572               Pseudo R2         =     0.0976

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                use3 |        RRR   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
0                    |  (base outcome)
---------------------+----------------------------------------------------------------
1                    |
          numchamber |   .6928983    .035658    -7.13   0.000     .6264189    .7664329
             nummask |   1.019751   .0870373     0.23   0.819      .862666    1.205439
                     |
                 age |
         18 or Less  |   584021.8   457042.4    16.97   0.000     125978.9     2707449
                 19  |   .1782823   .2119333    -1.45   0.147     .0173477    1.832209
                 20  |   1.290599   .9586582     0.34   0.731     .3009662    5.534326
                 21  |   .7351892    .409809    -0.55   0.581       .24656    2.192177
                 22  |   .8257647   .6510194    -0.24   0.808     .1761095     3.87195
                 23  |   .6180227   .5086236    -0.58   0.559     .1231624    3.101208
                 24  |   2.012123   1.091302     1.29   0.197     .6950143     5.82526
                 25  |   .6816992   .1886189    -1.38   0.166     .3963459    1.172496
              26-29  |   1.835586   .6128215     1.82   0.069     .9541041    3.531453
              30-34  |   1.987305   1.037957     1.31   0.189     .7139822    5.531487
                     |
       monthsoversea |
         3-6 Months  |    .519165   .3313797    -1.03   0.304     .1485897    1.813936
         6-9 Months  |   3.817712   2.149182     2.38   0.017      1.26654    11.50767
        9-12 Months  |   1.643739   .5844514     1.40   0.162     .8187999    3.299802
       12-18 Months  |   .7406722   .3592181    -0.62   0.536     .2862875    1.916239
       18-24 Months  |   1.984513   1.314608     1.03   0.301     .5417383     7.26973
       24-30 Months  |   .7512564   .4140464    -0.52   0.604     .2550693    2.212678
       30-36 Months  |   1.004535   1.171232     0.00   0.997     .1022143    9.872305
         36+ Months  |   1.152595   .7736945     0.21   0.832     .3092405    4.295928
                     |
              school |
         <8th Grade  |   28.79032   21.98519     4.40   0.000     6.445289    128.6028
          8th Grade  |   23.69701   28.45712     2.64   0.008     2.251686    249.3901
   Some High School  |   36.57604   40.82225     3.22   0.001     4.103713    325.9992
        High School  |   50.72687   52.19189     3.82   0.000     6.752323    381.0859
            College  |   19.39811   22.59835     2.55   0.011     1.977558    190.2784
                     |
           rankgrade |
       PRV. or PFC.  |   2.699786   3.014328     0.89   0.374     .3026616    24.08249
        CPL or TCH5  |   3.696754   5.482708     0.88   0.378     .2020187    67.64717
        SGT or TCH4  |   2.004365   2.088522     0.67   0.505     .2600374    15.44961
       SSGT or TCH3  |   3.901097   3.408796     1.56   0.119     .7037251    21.62572
   TSGT, MSGT, 1SGT  |   1.630587   1.813637     0.44   0.660     .1843257    14.42454
                     |
                base |
            Wheeler  |   .7029969   .2340913    -1.06   0.290     .3660255    1.350192
           Mokuleia  |   .4707841   .1825699    -1.94   0.052     .2201538     1.00674
            Bellows  |   .6610307   .1507043    -1.82   0.069     .4228258    1.033432
             Kahulu  |   .6719814   .1860724    -1.44   0.151     .3905338    1.156261
                     |
      ally_relations |   2.185488     .39255     4.35   0.000     1.536945    3.107695
orientation_officers |   .7307535   .1655512    -1.38   0.166     .4687403    1.139225
          infocenter |   1.133158   .5181818     0.27   0.785     .4624287    2.776748
         orient_meet |   2.138276   1.265747     1.28   0.199     .6701889     6.82229
        war_interest |    1.09463   .2185449     0.45   0.651     .7401564    1.618868
    honolulu_contact |   1.210163   .2438915     0.95   0.344     .8152597    1.796353
               _cons |   .1106025   .1369222    -1.78   0.075     .0097725    1.251769
---------------------+----------------------------------------------------------------
2                    |
          numchamber |   .8634227   .0629975    -2.01   0.044     .7483722    .9961604
             nummask |   1.044802   .1104132     0.41   0.678      .849337     1.28525
                     |
                 age |
         18 or Less  |   .2952042   .1458347    -2.47   0.014     .1121032    .7773689
                 19  |   .4052764   .4452705    -0.82   0.411      .047049    3.491021
                 20  |   1.278581   .4375914     0.72   0.473     .6537419    2.500633
                 21  |   .7169522   .3957923    -0.60   0.547     .2429885     2.11541
                 22  |   .3214885    .304933    -1.20   0.232     .0500954    2.063162
                 23  |   .7038502   .6036024    -0.41   0.682     .1310731     3.77961
                 24  |   2.506109   2.304519     1.00   0.318     .4133031    15.19607
                 25  |   .5841305   .3108063    -1.01   0.312      .205873    1.657373
              26-29  |   2.203871   1.382133     1.26   0.208       .64471    7.533691
              30-34  |   1.805367   .8565064     1.25   0.213     .7124208    4.575036
                     |
       monthsoversea |
         3-6 Months  |   .2499894   .1599275    -2.17   0.030     .0713471    .8759248
         6-9 Months  |     2.4371   1.965736     1.10   0.269     .5015384    11.84248
        9-12 Months  |   1.237665   .6196062     0.43   0.670     .4639491    3.301688
       12-18 Months  |   .3285396   .2463539    -1.48   0.138     .0755646    1.428424
       18-24 Months  |   1.257107   1.282577     0.22   0.823      .170186     9.28582
       24-30 Months  |   .3480822   .2191888    -1.68   0.094     .1013157    1.195878
       30-36 Months  |   .7006827   .2952868    -0.84   0.399     .3067622    1.600446
         36+ Months  |   .9076925   .7800078    -0.11   0.910     .1684517    4.891051
                     |
              school |
         <8th Grade  |   1.884552   2.413544     0.49   0.621     .1531353    23.19216
          8th Grade  |   1.241361   .6603186     0.41   0.684     .4376402    3.521103
   Some High School  |    2.97631   2.399412     1.35   0.176     .6130066    14.45078
        High School  |   3.059952   2.623037     1.30   0.192     .5702322    16.42016
            College  |    .998419   .8588853    -0.00   0.999      .184957    5.389581
                     |
           rankgrade |
       PRV. or PFC.  |   1.11e+07   2.19e+07     8.21   0.000     231019.6    5.32e+08
        CPL or TCH5  |   1.09e+07   2.24e+07     7.91   0.000     197054.5    6.07e+08
        SGT or TCH4  |    6869104   1.38e+07     7.82   0.000     132822.7    3.55e+08
       SSGT or TCH3  |    9854234   1.73e+07     9.16   0.000       313801    3.09e+08
   TSGT, MSGT, 1SGT  |    5110807    7894012    10.00   0.000     247597.9    1.05e+08
                     |
                base |
            Wheeler  |   1.091677   .4122569     0.23   0.816     .5207744    2.288436
           Mokuleia  |   .8192784   .3806668    -0.43   0.668     .3295581    2.036718
            Bellows  |     .59582   .1005028    -3.07   0.002     .4280894    .8292694
             Kahulu  |   .7922517   .2111461    -0.87   0.382     .4699007    1.335735
                     |
      ally_relations |   2.203607   .2459053     7.08   0.000     1.770707    2.742342
orientation_officers |   .7608021   .1496181    -1.39   0.164     .5174588    1.118582
          infocenter |   1.169603   .4701661     0.39   0.697     .5319437    2.571645
         orient_meet |   2.095403   1.088657     1.42   0.154     .7568846    5.801034
        war_interest |    1.00352   .2723696     0.01   0.990     .5895194    1.708261
    honolulu_contact |   .8637174   .2495834    -0.51   0.612     .4902357    1.521733
               _cons |   1.78e-07   3.17e-07    -8.73   0.000     5.44e-09    5.84e-06
--------------------------------------------------------------------------------------
Note: _cons estimates baseline relative risk for each outcome.
Note: 2 observations completely determined.  Standard errors questionable.
(est7 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Local "Yes"

added macro:
              e(Local) : "Yes"

.         estadd local Base "Yes"

added macro:
               e(Base) : "Yes"

.         
. esttab using "${result}/CW_Multinomial.tex", style(tex) noomitted b(3) se(3) nonotes eform keep(numchamber nummask) stats(N aic cov
>  Demog ForRel Officer Info Local Base, fmt(0 0 3 3) labels("Observations" "AIC" "\hline &\\ \textsc{Parameters}" "\hspace{3mm}Demog
> raphics" "\hspace{3mm}Postwar Foreign Policy" "\hspace{3mm}Officers' Leadership" "\hspace{3mm}Information Access" "\hspace{3mm}Loca
> l Contact" "\hspace{3mm}Airbase FE")) varlabels(numchamber "& \\ Gas Exposure" nummask "& \\ Gas Mask Training") starlevels(* .10 *
> * .05 *** .01) nolines prehead(\begin{tabular}{l*{16}{c}} \hline & \\ & & \multicolumn{14}{c}{\textbf{Support for Second-Use of Che
> mical Weapons Against Japan (=1)}} \\ \cline{3-16} & \\) posthead(\hline) prefoot() postfoot(& \\ \hline \end{tabular}) extracols(1
>  2 3 4 5 6 7) nomtit replace
(output written to ~/Desktop/JOP Replication/Results/CW_Multinomial.tex)

. 
. eststo clear

. 
. ********************************************************************************
. *                                                                       ADDED CONTROLS                                             
>         *
. ********************************************************************************
. 
. eststo clear

. 
. eststo: reghdfe use numchamber nummask ally_relations orientation_officers infocenter orient_meet war_interest honolulu_contact, cl
> uster(base) abs($demographic base)
(MWFE estimator converged in 9 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        634
Absorbing 5 HDFE groups                           F(   8,      4) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.0840
                                                  Adj R-squared   =     0.0172
                                                  Within R-sq.    =     0.0367
Number of clusters (base)    =          5         Root MSE        =     0.2882

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |   -.026248   .0068786    -3.82   0.019     -.045346     -.00715
             nummask |   .0024635   .0064859     0.38   0.723    -.0155442    .0204713
      ally_relations |   .0430913   .0080686     5.34   0.006     .0206894    .0654932
orientation_officers |  -.0228486   .0167005    -1.37   0.243    -.0692167    .0235195
          infocenter |   .0110018   .0325469     0.34   0.752    -.0793629    .1013665
         orient_meet |   .0616907   .0346553     1.78   0.150    -.0345279    .1579093
        war_interest |   .0033358   .0166075     0.20   0.851     -.042774    .0494456
    honolulu_contact |   .0103066    .016226     0.64   0.560     -.034744    .0553572
               _cons |   .8938628   .0179752    49.73   0.000     .8439557    .9437698
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
          base |         5           5           0    *|
-------------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation
(est1 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Local "Yes"

added macro:
              e(Local) : "Yes"

.         estadd local Base "Yes"

added macro:
               e(Base) : "Yes"

.         
. eststo: reghdfe use numchamber nummask ally_relations orientation_officers infocenter orient_meet war_interest honolulu_contact [pw
> =demweight], cluster(base) abs($demographic base)
(MWFE estimator converged in 9 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        634
Absorbing 5 HDFE groups                           F(   8,      4) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.0875
                                                  Adj R-squared   =     0.0210
                                                  Within R-sq.    =     0.0357
Number of clusters (base)    =          5         Root MSE        =     0.2878

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0282517   .0084726    -3.33   0.029    -.0517753    -.004728
             nummask |   .0011399   .0075527     0.15   0.887    -.0198297    .0221095
      ally_relations |    .040064   .0106586     3.76   0.020      .010471     .069657
orientation_officers |  -.0171191   .0198487    -0.86   0.437     -.072228    .0379898
          infocenter |   .0242342   .0287279     0.84   0.446    -.0555271    .1039955
         orient_meet |   .0715674   .0404272     1.77   0.151    -.0406766    .1838113
        war_interest |  -.0026486   .0152466    -0.17   0.871    -.0449799    .0396827
    honolulu_contact |   .0139053   .0133887     1.04   0.358    -.0232676    .0510782
               _cons |   .8836258   .0250217    35.31   0.000     .8141544    .9530972
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
          base |         5           5           0    *|
-------------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation
(est2 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Local "Yes"

added macro:
              e(Local) : "Yes"

.         estadd local Base "Yes"

added macro:
               e(Base) : "Yes"

.         estadd local DemWeight "Yes"

added macro:
          e(DemWeight) : "Yes"

.         
. eststo: reghdfe use numchamber nummask ally_relations orientation_officers infocenter orient_meet war_interest honolulu_contact tra
> ining newmask, cluster(base) abs($demographic base)
(MWFE estimator converged in 9 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        634
Absorbing 5 HDFE groups                           F(  10,      4) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.0866
                                                  Adj R-squared   =     0.0167
                                                  Within R-sq.    =     0.0395
Number of clusters (base)    =          5         Root MSE        =     0.2883

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0324961   .0124331    -2.61   0.059     -.067016    .0020237
             nummask |   .0044795   .0078713     0.57   0.600    -.0173748    .0263339
      ally_relations |   .0444728   .0077129     5.77   0.004     .0230585    .0658871
orientation_officers |  -.0228958   .0166045    -1.38   0.240    -.0689973    .0232057
          infocenter |   .0089882   .0293822     0.31   0.775    -.0725898    .0905661
         orient_meet |   .0576459   .0358983     1.61   0.184    -.0420237    .1573155
        war_interest |   .0034802   .0169636     0.21   0.847    -.0436184    .0505788
    honolulu_contact |   .0094886   .0156414     0.61   0.577     -.033939    .0529162
            training |   .0406784   .0304021     1.34   0.252    -.0437313     .125088
             newmask |   -.013453   .0325822    -0.41   0.701    -.1039157    .0770096
               _cons |   .8961566   .0350182    25.59   0.000     .7989305    .9933826
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
          base |         5           5           0    *|
-------------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation
(est3 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Local "Yes"

added macro:
              e(Local) : "Yes"

.         estadd local Base "Yes"

added macro:
               e(Base) : "Yes"

.         estadd local GasTrain "Yes"

added macro:
           e(GasTrain) : "Yes"

. 
. eststo: reghdfe use numchamber nummask ally_relations orientation_officers infocenter orient_meet war_interest honolulu_contact hig
> hprotect highconfid, cluster(base) abs($demographic base)
(MWFE estimator converged in 9 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        630
Absorbing 5 HDFE groups                           F(  10,      4) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.0875
                                                  Adj R-squared   =     0.0172
                                                  Within R-sq.    =     0.0404
Number of clusters (base)    =          5         Root MSE        =     0.2868

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0251714   .0072219    -3.49   0.025    -.0452227   -.0051201
             nummask |   .0018454   .0066704     0.28   0.796    -.0166746    .0203655
      ally_relations |   .0411865   .0087955     4.68   0.009     .0167664    .0656066
orientation_officers |   -.023756   .0164392    -1.45   0.222    -.0693986    .0218866
          infocenter |   .0164763   .0287884     0.57   0.598    -.0634531    .0964057
         orient_meet |   .0572183   .0387734     1.48   0.214     -.050434    .1648707
        war_interest |   .0033047   .0169021     0.20   0.855    -.0436229    .0502323
    honolulu_contact |   .0111982   .0159107     0.70   0.520    -.0329771    .0553734
         highprotect |   .0429838   .0224365     1.92   0.128    -.0193099    .1052775
          highconfid |   -.051078   .0193297    -2.64   0.057    -.1047459    .0025899
               _cons |   .9012014   .0225772    39.92   0.000     .8385171    .9638857
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
          base |         5           5           0    *|
-------------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation
(est4 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Local "Yes"

added macro:
              e(Local) : "Yes"

.         estadd local Base "Yes"

added macro:
               e(Base) : "Yes"

.         estadd local Confid "Yes"

added macro:
             e(Confid) : "Yes"

. 
. eststo: reghdfe use numchamber nummask ally_relations orientation_officers infocenter orient_meet war_interest honolulu_contact cor
> rect, cluster(base) abs($demographic base)
(MWFE estimator converged in 9 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        634
Absorbing 5 HDFE groups                           F(   9,      4) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.0903
                                                  Adj R-squared   =     0.0223
                                                  Within R-sq.    =     0.0434
Number of clusters (base)    =          5         Root MSE        =     0.2875

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0259986   .0067327    -3.86   0.018    -.0446917   -.0073056
             nummask |   .0023537   .0067764     0.35   0.746    -.0164606     .021168
      ally_relations |   .0440278   .0077681     5.67   0.005       .02246    .0655955
orientation_officers |  -.0214588   .0163934    -1.31   0.261    -.0669741    .0240565
          infocenter |   .0058974   .0325936     0.18   0.865     -.084597    .0963919
         orient_meet |   .0601853   .0357631     1.68   0.168     -.039109    .1594796
        war_interest |   .0030212   .0168877     0.18   0.867    -.0438666    .0499089
    honolulu_contact |   .0095734   .0165406     0.58   0.594    -.0363507    .0554976
     correct_history |   .0546004    .020685     2.64   0.058    -.0028304    .1120312
               _cons |    .857907    .016345    52.49   0.000     .8125259    .9032881
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
          base |         5           5           0    *|
-------------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation
(est5 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Local "Yes"

added macro:
              e(Local) : "Yes"

.         estadd local Base "Yes"

added macro:
               e(Base) : "Yes"

.         estadd local History "Yes"

added macro:
            e(History) : "Yes"

.         
. eststo: reghdfe use numchamber nummask ally_relations orientation_officers infocenter orient_meet war_interest honolulu_contact ori
> ent_talk, cluster(base) abs($demographic base)
(MWFE estimator converged in 9 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        634
Absorbing 5 HDFE groups                           F(   9,      4) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.0840
                                                  Adj R-squared   =     0.0155
                                                  Within R-sq.    =     0.0367
Number of clusters (base)    =          5         Root MSE        =     0.2885

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0262501   .0068887    -3.81   0.019    -.0453762    -.007124
             nummask |   .0024646   .0064487     0.38   0.722      -.01544    .0203692
      ally_relations |   .0430906   .0080044     5.38   0.006     .0208669    .0653144
orientation_officers |   -.022855   .0168026    -1.36   0.245    -.0695065    .0237965
          infocenter |   .0109941   .0336454     0.33   0.760    -.0824204    .1044086
         orient_meet |   .0616785   .0331091     1.86   0.136    -.0302471     .153604
        war_interest |   .0033302   .0158505     0.21   0.844    -.0406778    .0473382
    honolulu_contact |   .0103076   .0159985     0.64   0.554    -.0341113    .0547265
         orient_talk |   .0000756   .0212466     0.00   0.997    -.0589145    .0590657
               _cons |   .8938371   .0190359    46.96   0.000     .8409849    .9466892
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
          base |         5           5           0    *|
-------------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation
(est6 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Local "Yes"

added macro:
              e(Local) : "Yes"

.         estadd local Base "Yes"

added macro:
               e(Base) : "Yes"

.         estadd local Cohesion "Yes"

added macro:
           e(Cohesion) : "Yes"

. 
. eststo: reghdfe use numchamber nummask ally_relations orientation_officers infocenter orient_meet war_interest honolulu_contact mas
> kimpt chamberimpt gas_training, cluster(base) abs($demographic base)
(MWFE estimator converged in 9 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        634
Absorbing 5 HDFE groups                           F(  11,      4) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.0968
                                                  Adj R-squared   =     0.0260
                                                  Within R-sq.    =     0.0502
Number of clusters (base)    =          5         Root MSE        =     0.2869

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0230785   .0052006    -4.44   0.011    -.0375175   -.0086395
             nummask |  -.0001782   .0063383    -0.03   0.979    -.0177762    .0174198
      ally_relations |   .0429634   .0084362     5.09   0.007     .0195408     .066386
orientation_officers |  -.0254158   .0164773    -1.54   0.198     -.071164    .0203324
          infocenter |   .0073816   .0318254     0.23   0.828    -.0809799    .0957431
         orient_meet |   .0669634   .0346131     1.93   0.125     -.029138    .1630647
        war_interest |   .0012954   .0182909     0.07   0.947    -.0494882     .052079
    honolulu_contact |   .0074417   .0165139     0.45   0.676    -.0384082    .0532915
            maskimpt |   .0765608   .0364199     2.10   0.103    -.0245571    .1776786
         chamberimpt |  -.0546357   .0301868    -1.81   0.145    -.1384476    .0291762
        gas_training |  -.0037891   .0215937    -0.18   0.869    -.0637428    .0561647
               _cons |   .8944994   .0348189    25.69   0.000     .7978267     .991172
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
          base |         5           5           0    *|
-------------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation
(est7 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Local "Yes"

added macro:
              e(Local) : "Yes"

.         estadd local Base "Yes"

added macro:
               e(Base) : "Yes"

.         estadd local Important "Yes"

added macro:
          e(Important) : "Yes"

. 
. eststo: reghdfe use numchamber nummask ally_relations orientation_officers infocenter orient_meet war_interest honolulu_contact phy
> sical_training military_training, cluster(base) abs($demographic base)
(MWFE estimator converged in 9 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        634
Absorbing 5 HDFE groups                           F(  10,      4) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.0923
                                                  Adj R-squared   =     0.0228
                                                  Within R-sq.    =     0.0455
Number of clusters (base)    =          5         Root MSE        =     0.2874

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0271147   .0076936    -3.52   0.024    -.0484755   -.0057538
             nummask |   .0016841   .0063328     0.27   0.803    -.0158986    .0192669
      ally_relations |   .0465713   .0088619     5.26   0.006     .0219668    .0711757
orientation_officers |  -.0239766   .0158441    -1.51   0.205    -.0679668    .0200136
          infocenter |   .0067117   .0331777     0.20   0.850    -.0854043    .0988278
         orient_meet |   .0589468   .0338846     1.74   0.157    -.0351319    .1530256
        war_interest |   .0025757   .0183661     0.14   0.895    -.0484167    .0535682
    honolulu_contact |    .009753   .0162586     0.60   0.581    -.0353881    .0548941
   physical_training |  -.0243981   .0133686    -1.83   0.142    -.0615153    .0127192
   military_training |   .0338708   .0175478     1.93   0.126    -.0148497    .0825913
               _cons |   .8999605    .016882    53.31   0.000     .8530884    .9468325
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
          base |         5           5           0    *|
-------------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation
(est8 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Local "Yes"

added macro:
              e(Local) : "Yes"

.         estadd local Base "Yes"

added macro:
               e(Base) : "Yes"

.         estadd local MilTrain "Yes"

added macro:
           e(MilTrain) : "Yes"

.         
. esttab using "${result}/CW_Controls.tex", style(tex) b(3) se(3) nonotes keep(numchamber nummask) stats(N aic cov Demog ForRel Offic
> er Info Local Base DemWeight GasTrain Confid History Cohesion Important MilTrain, fmt(0 0 3 3) labels("Observations" "AIC" "\hline 
> &\\ \textsc{Parameters}" "\hspace{3mm}Demographics" "\hspace{3mm}Postwar Foreign Policy" "\hspace{3mm}Officers' Leadership" "\hspac
> e{3mm}Information Access" "\hspace{3mm}Local Contact" "\hspace{3mm}Airbase FE" "\hspace{3mm}Demographic Weights" "\hspace{3mm}Chemi
> cal Training Details" "\hspace{3mm}Chemical Readiness" "\hspace{3mm}History of Use" "\hspace{3mm}Unit Cohesion" "\hspace{3mm}Chemic
> al Training Importance" "\hspace{3mm}Military Training Importance")) varlabels(numchamber "& \\ Gas Exposure" nummask "& \\ Gas Mas
> k Training") starlevels(* .10 ** .05 *** .01) nolines prehead(\begin{tabular}{l*{11}{c}} \hline & \\ & & \multicolumn{9}{c}{\textbf
> {Support for Using Chemical Weapons Against Japan (=1)}} \\ \cline{3-11} & \\ & & \textbf{Baseline} & & \multicolumn{7}{c}{\textbf{
> Additional Controls}}\\ \cline{2-3} \cline{5-11} & \\) posthead(\hline) prefoot() postfoot(& \\ \hline \end{tabular}) extracols(1 2
> ) nomtit replace
(output written to ~/Desktop/JOP Replication/Results/CW_Controls.tex)

. 
. eststo clear

. 
. ********************************************************************************
. *                                                                               PROBIT                                             
>                 *
. ********************************************************************************
. 
. eststo clear

.         
. eststo: probit use numchamber nummask, cluster(base)

Iteration 0:   log pseudolikelihood = -196.26155  
Iteration 1:   log pseudolikelihood = -194.63085  
Iteration 2:   log pseudolikelihood = -194.62719  
Iteration 3:   log pseudolikelihood = -194.62719  

Probit regression                               Number of obs     =        634
                                                Wald chi2(2)      =      17.50
                                                Prob > chi2       =     0.0002
Log pseudolikelihood = -194.62719               Pseudo R2         =     0.0083

                                   (Std. Err. adjusted for 5 clusters in base)
------------------------------------------------------------------------------
             |               Robust
         use |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  numchamber |   -.107809   .0447494    -2.41   0.016    -.1955161   -.0201018
     nummask |   .0398808   .0341304     1.17   0.243    -.0270135    .1067752
       _cons |   1.378212   .0490726    28.09   0.000     1.282032    1.474393
------------------------------------------------------------------------------
(est1 stored)

. 
. eststo: probit use numchamber nummask i.($demographic), cluster(base)

note: 1.age != 0 predicts success perfectly
      1.age dropped and 2 obs not used

note: 0.monthsoversea != 0 predicts success perfectly
      0.monthsoversea dropped and 3 obs not used

note: 9.monthsoversea omitted because of collinearity
Iteration 0:   log pseudolikelihood = -195.77113  
Iteration 1:   log pseudolikelihood = -181.10815  
Iteration 2:   log pseudolikelihood = -180.70103  
Iteration 3:   log pseudolikelihood = -180.69965  
Iteration 4:   log pseudolikelihood = -180.69965  

Probit regression                               Number of obs     =        629
                                                Wald chi2(3)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -180.69965               Pseudo R2         =     0.0770

                                        (Std. Err. adjusted for 5 clusters in base)
-----------------------------------------------------------------------------------
                  |               Robust
              use |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
       numchamber |   -.118285   .0242227    -4.88   0.000    -.1657607   -.0708093
          nummask |   .0297863   .0352348     0.85   0.398    -.0392728    .0988453
                  |
              age |
      18 or Less  |          0  (empty)
              19  |  -.6840359   1.193678    -0.57   0.567    -3.023601    1.655529
              20  |    .179446   .7568558     0.24   0.813    -1.303964    1.662856
              21  |  -.0138778   .8169773    -0.02   0.986    -1.615124    1.587368
              22  |  -.1439208   .8254577    -0.17   0.862    -1.761788    1.473947
              23  |  -.2465031   .6040141    -0.41   0.683    -1.430349    .9373428
              24  |   .3511287   .8314367     0.42   0.673    -1.278457    1.980715
              25  |  -.1298624    .776961    -0.17   0.867    -1.652678    1.392953
           26-29  |   .3702806   .8232495     0.45   0.653    -1.243259     1.98382
           30-34  |   .4578339   .6802667     0.67   0.501    -.8754644    1.791132
             35+  |   .0050192   .6956013     0.01   0.994    -1.358334    1.368373
                  |
           school |
      <8th Grade  |   .7022811    .303578     2.31   0.021     .1072793    1.297283
       8th Grade  |   .5495707   .4972807     1.11   0.269    -.4250815    1.524223
Some High School  |   .7348719   .4142627     1.77   0.076    -.0770681    1.546812
     High School  |   .9146034   .2518865     3.63   0.000      .420915    1.408292
         College  |   .4105336   .3722105     1.10   0.270    -.3189857    1.140053
                  |
    monthsoversea |
       No Answer  |          0  (empty)
3 Months or Less  |    -.10838   .3848154    -0.28   0.778    -.8626044    .6458444
      3-6 Months  |  -.4607291   .1918244    -2.40   0.016    -.8366981   -.0847601
      6-9 Months  |   .4246402    .405146     1.05   0.295    -.3694313    1.218712
     9-12 Months  |   .0178763   .2094982     0.09   0.932    -.3927326    .4284853
    12-18 Months  |  -.3525472   .1093137    -3.23   0.001    -.5667981   -.1382963
    18-24 Months  |   .1848794   .2468154     0.75   0.454    -.2988699    .6686287
    24-30 Months  |  -.3290032    .135952    -2.42   0.016    -.5954642   -.0625422
    30-36 Months  |  -.0924915   .4070657    -0.23   0.820    -.8903256    .7053427
      36+ Months  |          0  (omitted)
                  |
        rankgrade |
    PRV. or PFC.  |   .5707359   .7000793     0.82   0.415    -.8013942    1.942866
     CPL or TCH5  |   .6089999   .8326575     0.73   0.465    -1.022979    2.240979
     SGT or TCH4  |   .3480797   .7363756     0.47   0.636     -1.09519    1.791349
    SSGT or TCH3  |   .5282025   .6035644     0.88   0.381     -.654762    1.711167
TSGT, MSGT, 1SGT  |   .2093433   .6853082     0.31   0.760    -1.133836    1.552523
                  |
            _cons |   .2162634   .6356945     0.34   0.734    -1.029675    1.462202
-----------------------------------------------------------------------------------
(est2 stored)

.         estadd local Demog "Yes" 

added macro:
              e(Demog) : "Yes"

. 
. eststo: probit use numchamber nummask ally_relations i.($demographic), cluster(base)

note: 1.age != 0 predicts success perfectly
      1.age dropped and 2 obs not used

note: 0.monthsoversea != 0 predicts success perfectly
      0.monthsoversea dropped and 3 obs not used

note: 9.monthsoversea omitted because of collinearity
Iteration 0:   log pseudolikelihood = -195.77113  
Iteration 1:   log pseudolikelihood = -174.85043  
Iteration 2:   log pseudolikelihood = -173.79353  
Iteration 3:   log pseudolikelihood =  -173.7899  
Iteration 4:   log pseudolikelihood =  -173.7899  

Probit regression                               Number of obs     =        629
                                                Wald chi2(3)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood =  -173.7899               Pseudo R2         =     0.1123

                                        (Std. Err. adjusted for 5 clusters in base)
-----------------------------------------------------------------------------------
                  |               Robust
              use |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
       numchamber |  -.1105571   .0150782    -7.33   0.000    -.1401099   -.0810043
          nummask |   .0279697   .0384418     0.73   0.467    -.0473749    .1033143
   ally_relations |   .3530171   .0548801     6.43   0.000     .2454541      .46058
                  |
              age |
      18 or Less  |          0  (empty)
              19  |  -.8039171   1.106229    -0.73   0.467    -2.972086    1.364251
              20  |    .007206   .7129589     0.01   0.992    -1.390168     1.40458
              21  |   -.258923   .7816027    -0.33   0.740    -1.790836     1.27299
              22  |  -.3187356   .7767476    -0.41   0.682    -1.841133    1.203662
              23  |  -.3675035   .5520113    -0.67   0.506    -1.449426    .7144188
              24  |    .209002   .7612485     0.27   0.784    -1.283018    1.701022
              25  |  -.2970539   .7490201    -0.40   0.692    -1.765106    1.170999
           26-29  |   .1614588   .7631245     0.21   0.832    -1.334238    1.657155
           30-34  |   .2743215   .6061899     0.45   0.651     -.913789    1.462432
             35+  |  -.1769868   .6284837    -0.28   0.778    -1.408792    1.054819
                  |
           school |
      <8th Grade  |     .85385   .3695149     2.31   0.021     .1296142    1.578086
       8th Grade  |   .7991784   .3908197     2.04   0.041     .0331859    1.565171
Some High School  |   1.056162   .3600847     2.93   0.003     .3504087    1.761915
     High School  |   1.223786   .2228975     5.49   0.000     .7869148    1.660657
         College  |    .730881   .3447653     2.12   0.034     .0551534    1.406609
                  |
    monthsoversea |
       No Answer  |          0  (empty)
3 Months or Less  |   -.163864   .4179253    -0.39   0.695    -.9829826    .6552547
      3-6 Months  |  -.5145087   .1992704    -2.58   0.010    -.9050715   -.1239459
      6-9 Months  |   .4744654   .4169045     1.14   0.255    -.3426524    1.291583
     9-12 Months  |   .0349816   .2319882     0.15   0.880    -.4197069    .4896702
    12-18 Months  |  -.3889911    .110546    -3.52   0.000    -.6056573   -.1723249
    18-24 Months  |   .1132817   .2063954     0.55   0.583    -.2912459    .5178092
    24-30 Months  |  -.3250106   .1448472    -2.24   0.025    -.6089059   -.0411152
    30-36 Months  |  -.1340274   .4040777    -0.33   0.740    -.9260051    .6579504
      36+ Months  |          0  (omitted)
                  |
        rankgrade |
    PRV. or PFC.  |   .6456943   .6160422     1.05   0.295    -.5617261    1.853115
     CPL or TCH5  |   .7125231   .7572346     0.94   0.347    -.7716294    2.196676
     SGT or TCH4  |    .392498    .639094     0.61   0.539    -.8601033    1.645099
    SSGT or TCH3  |   .6605518   .5342244     1.24   0.216    -.3865088    1.707612
TSGT, MSGT, 1SGT  |   .2096817   .6453215     0.32   0.745    -1.055125    1.474489
                  |
            _cons |   .1065123   .6313631     0.17   0.866    -1.130937    1.343961
-----------------------------------------------------------------------------------
(est3 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

. 
. eststo: probit use numchamber nummask ally_relations orientation_officers i.($demographic), cluster(base)

note: 1.age != 0 predicts success perfectly
      1.age dropped and 2 obs not used

note: 0.monthsoversea != 0 predicts success perfectly
      0.monthsoversea dropped and 3 obs not used

note: 9.monthsoversea omitted because of collinearity
Iteration 0:   log pseudolikelihood = -195.77113  
Iteration 1:   log pseudolikelihood = -174.44329  
Iteration 2:   log pseudolikelihood = -173.34935  
Iteration 3:   log pseudolikelihood = -173.34556  
Iteration 4:   log pseudolikelihood = -173.34556  

Probit regression                               Number of obs     =        629
                                                Wald chi2(3)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -173.34556               Pseudo R2         =     0.1145

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0920078   .0253719    -3.63   0.000    -.1417359   -.0422797
             nummask |    .033435   .0354972     0.94   0.346    -.0361382    .1030082
      ally_relations |   .3631798   .0589274     6.16   0.000     .2476842    .4786754
orientation_officers |  -.0759959   .0690174    -1.10   0.271    -.2112675    .0592757
                     |
                 age |
         18 or Less  |          0  (empty)
                 19  |  -.8204339   1.118576    -0.73   0.463    -3.012803    1.371936
                 20  |  -.0282381   .7090153    -0.04   0.968    -1.417883    1.361406
                 21  |  -.2830257   .7658535    -0.37   0.712    -1.784071    1.218019
                 22  |  -.3339817   .7614048    -0.44   0.661    -1.826308    1.158344
                 23  |  -.3840052   .5401865    -0.71   0.477    -1.442751    .6747408
                 24  |   .1937589   .7566846     0.26   0.798    -1.289316    1.676833
                 25  |  -.2983129   .7532304    -0.40   0.692    -1.774617    1.177992
              26-29  |   .1489317   .7508493     0.20   0.843    -1.322706    1.620569
              30-34  |   .2639179   .6073325     0.43   0.664     -.926432    1.454268
                35+  |  -.1811537   .6248838    -0.29   0.772    -1.405903    1.043596
                     |
              school |
         <8th Grade  |   .9866815   .3008888     3.28   0.001     .3969502    1.576413
          8th Grade  |   .8984305   .3856859     2.33   0.020     .1425001    1.654361
   Some High School  |   1.165772   .3637139     3.21   0.001     .4529059    1.878638
        High School  |   1.330551   .2000081     6.65   0.000     .9385424     1.72256
            College  |   .8472675   .3265029     2.59   0.009     .2073337    1.487201
                     |
       monthsoversea |
          No Answer  |          0  (empty)
   3 Months or Less  |  -.1733124    .440657    -0.39   0.694    -1.036984    .6903594
         3-6 Months  |  -.4643008    .188885    -2.46   0.014    -.8345087    -.094093
         6-9 Months  |   .4432934    .453723     0.98   0.329    -.4459874    1.332574
        9-12 Months  |   .0425682   .2481898     0.17   0.864    -.4438748    .5290113
       12-18 Months  |  -.3956249   .1395386    -2.84   0.005    -.6691156   -.1221342
       18-24 Months  |   .0943768   .2125969     0.44   0.657    -.3223055     .511059
       24-30 Months  |  -.3341204   .1731717    -1.93   0.054    -.6735307    .0052899
       30-36 Months  |  -.1481592   .4240493    -0.35   0.727    -.9792806    .6829622
         36+ Months  |          0  (omitted)
                     |
           rankgrade |
       PRV. or PFC.  |     .64038   .6191819     1.03   0.301    -.5731942    1.853954
        CPL or TCH5  |   .7090937   .7616633     0.93   0.352     -.783739    2.201926
        SGT or TCH4  |   .3919889   .6427281     0.61   0.542     -.867735    1.651713
       SSGT or TCH3  |   .6438381   .5456119     1.18   0.238    -.4255416    1.713218
   TSGT, MSGT, 1SGT  |   .1912479   .6606698     0.29   0.772    -1.103641    1.486137
                     |
               _cons |  -.0097743   .5748928    -0.02   0.986    -1.136544    1.116995
--------------------------------------------------------------------------------------
(est4 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

. 
. eststo: probit use numchamber nummask ally_relations orientation_officers infocenter orient_meet war_interest i.($demographic), clu
> ster(base)

note: 1.age != 0 predicts success perfectly
      1.age dropped and 2 obs not used

note: 0.monthsoversea != 0 predicts success perfectly
      0.monthsoversea dropped and 3 obs not used

note: 9.monthsoversea omitted because of collinearity
Iteration 0:   log pseudolikelihood = -195.77113  
Iteration 1:   log pseudolikelihood = -171.51349  
Iteration 2:   log pseudolikelihood = -170.27348  
Iteration 3:   log pseudolikelihood = -170.26949  
Iteration 4:   log pseudolikelihood = -170.26949  

Probit regression                               Number of obs     =        629
                                                Wald chi2(3)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -170.26949               Pseudo R2         =     0.1303

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.1650133    .031247    -5.28   0.000    -.2262563   -.1037704
             nummask |   .0258789   .0311962     0.83   0.407    -.0352644    .0870222
      ally_relations |   .3589293   .0608379     5.90   0.000     .2396892    .4781695
orientation_officers |  -.1295068    .091288    -1.42   0.156    -.3084279    .0494144
          infocenter |   .1403426   .1621159     0.87   0.387    -.1773987     .458084
         orient_meet |   .3663996   .1919527     1.91   0.056    -.0098208      .74262
        war_interest |   .0282175   .1041426     0.27   0.786    -.1758982    .2323332
                     |
                 age |
         18 or Less  |          0  (empty)
                 19  |  -.9785594   .9298374    -1.05   0.293    -2.801007    .8438884
                 20  |  -.1482702   .5824517    -0.25   0.799    -1.289854    .9933141
                 21  |   -.384693   .5401137    -0.71   0.476    -1.443296    .6739104
                 22  |  -.4834209   .5175773    -0.93   0.350    -1.497854     .531012
                 23  |  -.4781303    .357823    -1.34   0.181    -1.179451      .22319
                 24  |   .1389275    .611894     0.23   0.820    -1.060363    1.338218
                 25  |  -.3964682   .5257736    -0.75   0.451    -1.426965    .6340291
              26-29  |   .0642009   .5041238     0.13   0.899    -.9238636    1.052265
              30-34  |   .1780095    .373383     0.48   0.634    -.5538077    .9098266
                35+  |  -.2116457   .4058527    -0.52   0.602    -1.007102     .583811
                     |
              school |
         <8th Grade  |   1.034173   .2337068     4.43   0.000      .576116     1.49223
          8th Grade  |   .8910064   .3713525     2.40   0.016     .1631687    1.618844
   Some High School  |   1.166441   .3368668     3.46   0.001     .5061942    1.826688
        High School  |   1.326192   .1689576     7.85   0.000     .9950411    1.657343
            College  |   .8523296   .2859379     2.98   0.003     .2919016    1.412758
                     |
       monthsoversea |
          No Answer  |          0  (empty)
   3 Months or Less  |  -.0574115   .3713079    -0.15   0.877    -.7851615    .6703386
         3-6 Months  |  -.5114991   .1997831    -2.56   0.010    -.9030668   -.1199314
         6-9 Months  |   .5048216   .4198563     1.20   0.229    -.3180816    1.327725
        9-12 Months  |   .1213282   .2500043     0.49   0.627    -.3686712    .6113276
       12-18 Months  |  -.3575401   .1197398    -2.99   0.003    -.5922258   -.1228543
       18-24 Months  |   .1526707     .17348     0.88   0.379    -.1873439    .4926852
       24-30 Months  |   -.353152    .166066    -2.13   0.033    -.6786353   -.0276687
       30-36 Months  |  -.0709447   .4098439    -0.17   0.863    -.8742241    .7323346
         36+ Months  |          0  (omitted)
                     |
           rankgrade |
       PRV. or PFC.  |   .6814429   .6162577     1.11   0.269       -.5264    1.889286
        CPL or TCH5  |   .7733693   .7768664     1.00   0.319     -.749261    2.295999
        SGT or TCH4  |   .4478812    .623034     0.72   0.472     -.773243    1.669005
       SSGT or TCH3  |   .7305359   .5618849     1.30   0.194    -.3707383     1.83181
   TSGT, MSGT, 1SGT  |   .3602037   .6842508     0.53   0.599    -.9809032    1.701311
                     |
               _cons |  -.1778172   .5710911    -0.31   0.756    -1.297135    .9415008
--------------------------------------------------------------------------------------
(est5 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

. 
. eststo: probit use numchamber nummask ally_relations orientation_officers infocenter orient_meet war_interest honolulu_contact i.($
> demographic), cluster(base)

note: 1.age != 0 predicts success perfectly
      1.age dropped and 2 obs not used

note: 0.monthsoversea != 0 predicts success perfectly
      0.monthsoversea dropped and 3 obs not used

note: 9.monthsoversea omitted because of collinearity
Iteration 0:   log pseudolikelihood = -195.77113  
Iteration 1:   log pseudolikelihood = -171.09252  
Iteration 2:   log pseudolikelihood = -169.80765  
Iteration 3:   log pseudolikelihood = -169.80348  
Iteration 4:   log pseudolikelihood = -169.80348  

Probit regression                               Number of obs     =        629
                                                Wald chi2(3)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -169.80348               Pseudo R2         =     0.1326

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.1645067   .0259503    -6.34   0.000    -.2153683   -.1136451
             nummask |   .0287181   .0318911     0.90   0.368    -.0337874    .0912235
      ally_relations |   .3530783   .0612513     5.76   0.000      .233028    .4731285
orientation_officers |  -.1373646   .0932673    -1.47   0.141     -.320165    .0454359
          infocenter |   .1360653   .1617139     0.84   0.400    -.1808882    .4530188
         orient_meet |   .3806905   .1887955     2.02   0.044      .010658     .750723
        war_interest |   .0279366   .1068599     0.26   0.794    -.1815049    .2373781
    honolulu_contact |   .0795794   .1009234     0.79   0.430    -.1182269    .2773857
                     |
                 age |
         18 or Less  |          0  (empty)
                 19  |  -.8925607   .8312755    -1.07   0.283    -2.521831    .7367094
                 20  |  -.0462391   .4784552    -0.10   0.923    -.9839941    .8915159
                 21  |  -.2938369   .4604411    -0.64   0.523    -1.196285    .6086111
                 22  |   -.381594   .4906477    -0.78   0.437    -1.343246    .5800577
                 23  |  -.3955116   .3685476    -1.07   0.283    -1.117852    .3268285
                 24  |   .2101666   .5395512     0.39   0.697    -.8473343    1.267667
                 25  |  -.3127019    .436972    -0.72   0.474    -1.169151    .5437476
              26-29  |   .1248844   .4387286     0.28   0.776    -.7350079    .9847766
              30-34  |   .2564258   .3112415     0.82   0.410    -.3535963    .8664479
                35+  |  -.1375353   .3252037    -0.42   0.672    -.7749228    .4998522
                     |
              school |
         <8th Grade  |   1.017605   .2114968     4.81   0.000     .6030787    1.432131
          8th Grade  |   .8788706   .3588301     2.45   0.014     .1755764    1.582165
   Some High School  |   1.152138   .3205474     3.59   0.000     .5238768      1.7804
        High School  |   1.305336   .1608825     8.11   0.000     .9900122     1.62066
            College  |    .834212   .2890386     2.89   0.004     .2677068    1.400717
                     |
       monthsoversea |
          No Answer  |          0  (empty)
   3 Months or Less  |  -.0226689   .3763306    -0.06   0.952    -.7602633    .7149256
         3-6 Months  |  -.4797442   .2020216    -2.37   0.018    -.8756991   -.0837892
         6-9 Months  |   .5539304    .431767     1.28   0.200    -.2923174    1.400178
        9-12 Months  |   .1705646   .2490595     0.68   0.493    -.3175831    .6587123
       12-18 Months  |  -.3243056   .1274105    -2.55   0.011    -.5740255   -.0745856
       18-24 Months  |   .2096012   .2141334     0.98   0.328    -.2100925    .6292948
       24-30 Months  |  -.3176132   .1658605    -1.91   0.055    -.6426939    .0074674
       30-36 Months  |  -.0081326   .3802233    -0.02   0.983    -.7533566    .7370913
         36+ Months  |          0  (omitted)
                     |
           rankgrade |
       PRV. or PFC.  |   .7633253   .5406185     1.41   0.158    -.2962675    1.822918
        CPL or TCH5  |   .8652762   .6893882     1.26   0.209    -.4858998    2.216452
        SGT or TCH4  |   .5488794   .5168032     1.06   0.288    -.4640363    1.561795
       SSGT or TCH3  |   .8314966   .4597205     1.81   0.070     -.069539    1.732532
   TSGT, MSGT, 1SGT  |   .4414734   .5939044     0.74   0.457    -.7225579    1.605505
                     |
               _cons |   -.374294   .3839346    -0.97   0.330    -1.126792     .378204
--------------------------------------------------------------------------------------
(est6 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Local "Yes"

added macro:
              e(Local) : "Yes"

. 
. eststo: probit use numchamber nummask ally_relations orientation_officers infocenter orient_meet war_interest honolulu_contact i.($
> demographic base), cluster(base)

note: 1.age != 0 predicts success perfectly
      1.age dropped and 2 obs not used

note: 0.monthsoversea != 0 predicts success perfectly
      0.monthsoversea dropped and 3 obs not used

note: 9.monthsoversea omitted because of collinearity
Iteration 0:   log pseudolikelihood = -195.77113  
Iteration 1:   log pseudolikelihood = -170.49423  
Iteration 2:   log pseudolikelihood = -169.15581  
Iteration 3:   log pseudolikelihood = -169.15115  
Iteration 4:   log pseudolikelihood = -169.15115  

Probit regression                               Number of obs     =        629
                                                Wald chi2(3)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -169.15115               Pseudo R2         =     0.1360

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.1591455   .0287196    -5.54   0.000    -.2154349   -.1028561
             nummask |   .0160289   .0430011     0.37   0.709    -.0682517    .1003095
      ally_relations |   .3551793   .0616377     5.76   0.000     .2343716    .4759871
orientation_officers |  -.1458041    .101706    -1.43   0.152    -.3451443     .053536
          infocenter |   .0978591   .1977593     0.49   0.621    -.2897421    .4854602
         orient_meet |   .3644899   .2409541     1.51   0.130    -.1077714    .8367513
        war_interest |   .0257854   .1066752     0.24   0.809    -.1832942     .234865
    honolulu_contact |   .0782688   .1016535     0.77   0.441    -.1209684     .277506
                     |
                 age |
         18 or Less  |          0  (empty)
                 19  |  -.8159368    .886198    -0.92   0.357    -2.552853    .9209794
                 20  |    .020303   .5164248     0.04   0.969     -.991871    1.032477
                 21  |  -.2332415   .5370845    -0.43   0.664    -1.285908    .8194248
                 22  |  -.3131468   .6084478    -0.51   0.607    -1.505683    .8793889
                 23  |  -.3495387   .4861509    -0.72   0.472    -1.302377    .6032995
                 24  |   .2555137   .6144969     0.42   0.678     -.948878    1.459905
                 25  |  -.3205219   .4878407    -0.66   0.511    -1.276672    .6356284
              26-29  |    .159072   .5086666     0.31   0.754    -.8378962     1.15604
              30-34  |   .2869671   .3833323     0.75   0.454    -.4643505    1.038285
                35+  |  -.1156511   .3734403    -0.31   0.757    -.8475807    .6162785
                     |
              school |
         <8th Grade  |   1.107126   .2193601     5.05   0.000     .6771878    1.537064
          8th Grade  |   1.000669   .3987095     2.51   0.012     .2192121    1.782125
   Some High School  |   1.257457   .3442841     3.65   0.000     .5826723    1.932241
        High School  |   1.416336   .1741853     8.13   0.000     1.074939    1.757733
            College  |   .9077657   .3318306     2.74   0.006     .2573896    1.558142
                     |
       monthsoversea |
          No Answer  |          0  (empty)
   3 Months or Less  |   .0126886    .363102     0.03   0.972    -.6989782    .7243555
         3-6 Months  |  -.4671142   .2040139    -2.29   0.022    -.8669741   -.0672542
         6-9 Months  |   .6562691   .4238493     1.55   0.122    -.1744602    1.486998
        9-12 Months  |   .2110797   .2165906     0.97   0.330      -.21343    .6355894
       12-18 Months  |  -.2534768   .0993489    -2.55   0.011     -.448197   -.0587565
       18-24 Months  |    .279612   .2414128     1.16   0.247    -.1935483    .7527723
       24-30 Months  |  -.2335973   .1303137    -1.79   0.073    -.4890074    .0218128
       30-36 Months  |   .0063867   .3828412     0.02   0.987    -.7439682    .7567417
         36+ Months  |          0  (omitted)
                     |
           rankgrade |
       PRV. or PFC.  |   .7547198   .5407507     1.40   0.163     -.305132    1.814572
        CPL or TCH5  |   .8915656   .6778054     1.32   0.188    -.4369085     2.22004
        SGT or TCH4  |   .5736919   .4926073     1.16   0.244    -.3918007    1.539185
       SSGT or TCH3  |   .8650848   .4232147     2.04   0.041     .0355993     1.69457
   TSGT, MSGT, 1SGT  |   .4608852   .5873323     0.78   0.433    -.6902649    1.612035
                     |
                base |
            Wheeler  |  -.1084673   .2324457    -0.47   0.641    -.5640526     .347118
           Mokuleia  |  -.2931775   .2746111    -1.07   0.286    -.8314054    .2450504
            Bellows  |  -.2444747   .1417482    -1.72   0.085     -.522296    .0333467
             Kahulu  |    -.17379   .1742456    -1.00   0.319    -.5153051    .1677251
                     |
               _cons |   -.374346   .3909247    -0.96   0.338    -1.140544    .3918524
--------------------------------------------------------------------------------------
(est7 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Local "Yes"

added macro:
              e(Local) : "Yes"

.         estadd local Base "Yes"

added macro:
               e(Base) : "Yes"

. 
. gen estimation_sample=(e(sample))

.         
. esttab using "${result}/CW_Probit.tex", style(tex) b(3) se(3) nonotes keep(numchamber nummask) stats(N aic cov Demog ForRel Officer
>  Info Local Base, fmt(0 0 3 3) labels("Observations" "AIC" "\hline &\\ \textsc{Parameters}" "\hspace{3mm}Demographics" "\hspace{3mm
> }Postwar Foreign Policy" "\hspace{3mm}Officers' Leadership" "\hspace{3mm}Information Access" "\hspace{3mm}Local Contact" "\hspace{3
> mm}Airbase FE")) varlabels(numchamber "& \\ Gas Exposure" nummask "& \\ Gas Mask Training") starlevels(* .10 ** .05 *** .01) noline
> s prehead(\begin{tabular}{l*{9}{c}} \hline & \\ & & \multicolumn{7}{c}{\textbf{Support for Using Chemical Weapons Against Japan (=1
> )}} \\ \cline{3-9} & \\) posthead(\hline) prefoot() postfoot(& \\ \hline \end{tabular}) extracols(1) nomtit replace
(output written to ~/Desktop/JOP Replication/Results/CW_Probit.tex)

. 
. eststo clear

.         
. eststo: reghdfe use numchamber nummask if estimation_sample==1, cluster(base) noabs
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =        629
Absorbing 1 HDFE group                            F(   2,      4) =       3.66
Statistics robust to heteroskedasticity           Prob > F        =     0.1248
                                                  R-squared       =     0.0053
                                                  Adj R-squared   =     0.0021
                                                  Within R-sq.    =     0.0053
Number of clusters (base)    =          5         Root MSE        =     0.2915

                                   (Std. Err. adjusted for 5 clusters in base)
------------------------------------------------------------------------------
             |               Robust
         use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  numchamber |  -.0185005   .0085577    -2.16   0.097    -.0422604    .0052595
     nummask |   .0065095   .0054972     1.18   0.302    -.0087533    .0217723
       _cons |   .9147227   .0084517   108.23   0.000      .891257    .9381885
------------------------------------------------------------------------------
(est1 stored)

. 
. eststo: reghdfe use numchamber nummask if estimation_sample==1, cluster(base) abs($demographic)
(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =        629
Absorbing 4 HDFE groups                           F(   2,      4) =       8.74
Statistics robust to heteroskedasticity           Prob > F        =     0.0347
                                                  R-squared       =     0.0483
                                                  Adj R-squared   =     0.0006
                                                  Within R-sq.    =     0.0039
Number of clusters (base)    =          5         Root MSE        =     0.2917

                                   (Std. Err. adjusted for 5 clusters in base)
------------------------------------------------------------------------------
             |               Robust
         use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  numchamber |  -.0180458   .0057292    -3.15   0.035    -.0339527    -.002139
     nummask |   .0041825   .0067495     0.62   0.569     -.014557     .022922
       _cons |    .918961   .0171076    53.72   0.000     .8714628    .9664593
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        11           0          11     |
        school |         6           1           5     |
 monthsoversea |         9           1           8    ?|
     rankgrade |         6           1           5    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est2 stored)

.         estadd local Demog "Yes" 

added macro:
              e(Demog) : "Yes"

. 
. eststo: reghdfe use numchamber nummask ally_relations if estimation_sample==1, cluster(base) abs($demographic)
(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =        629
Absorbing 4 HDFE groups                           F(   3,      4) =     227.01
Statistics robust to heteroskedasticity           Prob > F        =     0.0001
                                                  R-squared       =     0.0669
                                                  Adj R-squared   =     0.0184
                                                  Within R-sq.    =     0.0233
Number of clusters (base)    =          5         Root MSE        =     0.2891

                                     (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------
               |               Robust
           use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
    numchamber |   -.016821   .0043039    -3.91   0.017    -.0287705   -.0048715
       nummask |   .0042349   .0067546     0.63   0.565    -.0145188    .0229885
ally_relations |    .041799   .0064784     6.45   0.003      .023812     .059786
         _cons |   .9175796   .0186554    49.19   0.000     .8657839    .9693754
--------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        11           0          11     |
        school |         6           1           5     |
 monthsoversea |         9           1           8    ?|
     rankgrade |         6           1           5    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est3 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

. 
. eststo: reghdfe use numchamber nummask ally_relations orientation_officers if estimation_sample==1, cluster(base) abs($demographic)
(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =        629
Absorbing 4 HDFE groups                           F(   4,      4) =     909.03
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.0683
                                                  Adj R-squared   =     0.0183
                                                  Within R-sq.    =     0.0248
Number of clusters (base)    =          5         Root MSE        =     0.2891

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0142388   .0057388    -2.48   0.068    -.0301722    .0016946
             nummask |   .0052329   .0063072     0.83   0.453    -.0122786    .0227445
      ally_relations |    .042973   .0063973     6.72   0.003     .0252112    .0607348
orientation_officers |  -.0117694   .0106263    -1.11   0.330    -.0412727    .0177339
               _cons |   .9126441   .0181466    50.29   0.000     .8622611    .9630272
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        11           0          11     |
        school |         6           1           5     |
 monthsoversea |         9           1           8    ?|
     rankgrade |         6           1           5    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est4 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

. 
. eststo: reghdfe use numchamber nummask ally_relations orientation_officers infocenter orient_meet war_interest if estimation_sample
> ==1, cluster(base) abs($demographic)
(MWFE estimator converged in 8 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        629
Absorbing 4 HDFE groups                           F(   7,      4) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.0798
                                                  Adj R-squared   =     0.0255
                                                  Within R-sq.    =     0.0368
Number of clusters (base)    =          5         Root MSE        =     0.2880

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0268857   .0077556    -3.47   0.026    -.0484187   -.0053527
             nummask |   .0036829   .0056971     0.65   0.553    -.0121349    .0195007
      ally_relations |   .0433182   .0070158     6.17   0.003     .0238393    .0627971
orientation_officers |  -.0202061   .0147431    -1.37   0.242    -.0611396    .0207275
          infocenter |   .0175494   .0291832     0.60   0.580    -.0634762    .0985751
         orient_meet |   .0647421   .0295138     2.19   0.093    -.0172015    .1466857
        war_interest |   .0046648   .0163778     0.28   0.790    -.0408073    .0501369
               _cons |   .8859964     .01795    49.36   0.000     .8361592    .9358336
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        11           0          11     |
        school |         6           1           5     |
 monthsoversea |         9           1           8    ?|
     rankgrade |         6           1           5    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est5 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

. 
. eststo: reghdfe use numchamber nummask ally_relations orientation_officers infocenter orient_meet war_interest honolulu_contact if 
> estimation_sample==1, cluster(base) abs($demographic)
(MWFE estimator converged in 8 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        629
Absorbing 4 HDFE groups                           F(   8,      4) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.0810
                                                  Adj R-squared   =     0.0251
                                                  Within R-sq.    =     0.0380
Number of clusters (base)    =          5         Root MSE        =     0.2881

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0265587    .007446    -3.57   0.023    -.0472322   -.0058852
             nummask |   .0041243    .005531     0.75   0.497    -.0112323    .0194809
      ally_relations |   .0425137   .0077089     5.51   0.005     .0211103    .0639171
orientation_officers |  -.0210517   .0151508    -1.39   0.237    -.0631172    .0210138
          infocenter |   .0160262     .02885     0.56   0.608    -.0640742    .0961266
         orient_meet |   .0654494   .0290098     2.26   0.087    -.0150948    .1459935
        war_interest |   .0040805   .0166942     0.24   0.819      -.04227    .0504309
    honolulu_contact |   .0106073   .0167786     0.63   0.562    -.0359775    .0571921
               _cons |    .885279   .0176964    50.03   0.000     .8361459    .9344122
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        11           0          11     |
        school |         6           1           5     |
 monthsoversea |         9           1           8    ?|
     rankgrade |         6           1           5    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est6 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Local "Yes"

added macro:
              e(Local) : "Yes"

. 
. eststo: reghdfe use numchamber nummask ally_relations orientation_officers infocenter orient_meet war_interest honolulu_contact if 
> estimation_sample==1, cluster(base) abs($demographic base)
(MWFE estimator converged in 9 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        629
Absorbing 5 HDFE groups                           F(   8,      4) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.0836
                                                  Adj R-squared   =     0.0196
                                                  Within R-sq.    =     0.0370
Number of clusters (base)    =          5         Root MSE        =     0.2889

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0263627   .0068365    -3.86   0.018    -.0453438   -.0073815
             nummask |   .0023314   .0064837     0.36   0.737    -.0156703    .0203331
      ally_relations |   .0431971   .0079856     5.41   0.006     .0210254    .0653687
orientation_officers |  -.0234044   .0167774    -1.39   0.235     -.069986    .0231772
          infocenter |   .0104035   .0322736     0.32   0.763    -.0792025    .1000095
         orient_meet |   .0623342   .0348574     1.79   0.148    -.0344456    .1591139
        war_interest |   .0033389   .0166339     0.20   0.851    -.0428443     .049522
    honolulu_contact |   .0103759   .0165413     0.63   0.565      -.03555    .0563019
               _cons |    .893589   .0181044    49.36   0.000     .8433232    .9438548
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        11           0          11     |
        school |         6           1           5     |
 monthsoversea |         9           1           8    ?|
     rankgrade |         6           1           5    ?|
          base |         5           5           0    *|
-------------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation
(est7 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Local "Yes"

added macro:
              e(Local) : "Yes"

.         estadd local Base "Yes"

added macro:
               e(Base) : "Yes"

.         
. esttab using "${result}/CW_Main_Sample.tex", style(tex) b(3) se(3) nonotes keep(numchamber nummask) stats(N aic cov Demog ForRel Of
> ficer Info Local Base, fmt(0 0 3 3) labels("Observations" "AIC" "\hline &\\ \textsc{Parameters}" "\hspace{3mm}Demographics" "\hspac
> e{3mm}Postwar Foreign Policy" "\hspace{3mm}Officers' Leadership" "\hspace{3mm}Information Access" "\hspace{3mm}Local Contact" "\hsp
> ace{3mm}Airbase FE")) varlabels(numchamber "& \\ Gas Exposure" nummask "& \\ Gas Mask Training") starlevels(* .10 ** .05 *** .01) n
> olines prehead(\begin{tabular}{l*{9}{c}} \hline & \\ & & \multicolumn{7}{c}{\textbf{Support for Using Chemical Weapons Against Japa
> n (=1)}} \\ \cline{3-9} & \\) posthead(\hline) prefoot() postfoot(& \\ \hline \end{tabular}) extracols(1) nomtit replace
(output written to ~/Desktop/JOP Replication/Results/CW_Main_Sample.tex)

. 
. eststo clear

. 
. ********************************************************************************
. *                                                               COMPLIANCE WEIGHT                                                  
>         *
. ********************************************************************************
. 
. eststo clear

.         
. eststo: reghdfe use numchamber nummask [pw=pr_complier], cluster(base) noabs
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =        634
Absorbing 1 HDFE group                            F(   2,      4) =       1.50
Statistics robust to heteroskedasticity           Prob > F        =     0.3259
                                                  R-squared       =     0.0099
                                                  Adj R-squared   =     0.0067
                                                  Within R-sq.    =     0.0099
Number of clusters (base)    =          5         Root MSE        =     0.2859

                                   (Std. Err. adjusted for 5 clusters in base)
------------------------------------------------------------------------------
             |               Robust
         use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  numchamber |  -.0238847   .0152637    -1.56   0.193    -.0662636    .0184941
     nummask |    .008994   .0056757     1.58   0.188    -.0067642    .0247523
       _cons |   .9236271   .0157719    58.56   0.000     .8798371     .967417
------------------------------------------------------------------------------
(est1 stored)

.         estadd local Complier "Yes"

added macro:
           e(Complier) : "Yes"

. 
. eststo: reghdfe use numchamber nummask [pw=pr_complier], cluster(base) abs($demographic)
(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =        634
Absorbing 4 HDFE groups                           F(   2,      4) =       3.10
Statistics robust to heteroskedasticity           Prob > F        =     0.1537
                                                  R-squared       =     0.0555
                                                  Adj R-squared   =     0.0052
                                                  Within R-sq.    =     0.0116
Number of clusters (base)    =          5         Root MSE        =     0.2861

                                   (Std. Err. adjusted for 5 clusters in base)
------------------------------------------------------------------------------
             |               Robust
         use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  numchamber |  -.0259672   .0104548    -2.48   0.068    -.0549944      .00306
     nummask |   .0108393   .0074651     1.45   0.220    -.0098872    .0315658
       _cons |   .9229235   .0186974    49.36   0.000     .8710112    .9748359
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est2 stored)

.         estadd local Demog "Yes" 

added macro:
              e(Demog) : "Yes"

.         estadd local Complier "Yes"

added macro:
           e(Complier) : "Yes"

. 
. eststo: reghdfe use numchamber nummask ally_relations [pw=pr_complier], cluster(base) abs($demographic)
(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =        634
Absorbing 4 HDFE groups                           F(   3,      4) =     146.15
Statistics robust to heteroskedasticity           Prob > F        =     0.0002
                                                  R-squared       =     0.0753
                                                  Adj R-squared   =     0.0245
                                                  Within R-sq.    =     0.0324
Number of clusters (base)    =          5         Root MSE        =     0.2833

                                     (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------
               |               Robust
           use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
    numchamber |  -.0236429    .010101    -2.34   0.079    -.0516878     .004402
       nummask |   .0110316   .0073105     1.51   0.206    -.0092656    .0313288
ally_relations |    .043757   .0082771     5.29   0.006     .0207761    .0667379
         _cons |   .9208511   .0180117    51.13   0.000     .8708427    .9708596
--------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est3 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Complier "Yes"

added macro:
           e(Complier) : "Yes"

. 
. eststo: reghdfe use numchamber nummask ally_relations orientation_officers [pw=pr_complier], cluster(base) abs($demographic)
(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =        634
Absorbing 4 HDFE groups                           F(   4,      4) =     497.75
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.0754
                                                  Adj R-squared   =     0.0229
                                                  Within R-sq.    =     0.0324
Number of clusters (base)    =          5         Root MSE        =     0.2835

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0231221   .0106789    -2.17   0.096    -.0527714    .0065272
             nummask |   .0111875   .0074282     1.51   0.207    -.0094364    .0318115
      ally_relations |   .0439923   .0086984     5.06   0.007     .0198418    .0681428
orientation_officers |  -.0021405   .0069409    -0.31   0.773    -.0214114    .0171304
               _cons |   .9199965   .0189975    48.43   0.000     .8672509     .972742
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est4 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Complier "Yes"

added macro:
           e(Complier) : "Yes"

. 
. eststo: reghdfe use numchamber nummask ally_relations orientation_officers infocenter orient_meet war_interest [pw=pr_complier], cl
> uster(base) abs($demographic)
(MWFE estimator converged in 8 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        634
Absorbing 4 HDFE groups                           F(   7,      4) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.0961
                                                  Adj R-squared   =     0.0400
                                                  Within R-sq.    =     0.0541
Number of clusters (base)    =          5         Root MSE        =     0.2811

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |   -.042976   .0188091    -2.28   0.084    -.0951984    .0092463
             nummask |   .0102921   .0082082     1.25   0.278    -.0124975    .0330817
      ally_relations |   .0448814   .0090127     4.98   0.008     .0198582    .0699045
orientation_officers |  -.0147945   .0081014    -1.83   0.142    -.0372877    .0076987
          infocenter |   .0230888   .0220815     1.05   0.355    -.0382192    .0843967
         orient_meet |   .0885366   .0247691     3.57   0.023     .0197665    .1573067
        war_interest |  -.0093822   .0118322    -0.79   0.472    -.0422337    .0234694
               _cons |   .8889751   .0189605    46.89   0.000     .8363324    .9416178
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est5 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Complier "Yes"

added macro:
           e(Complier) : "Yes"

. 
. eststo: reghdfe use numchamber nummask ally_relations orientation_officers infocenter orient_meet war_interest honolulu_contact [pw
> =pr_complier], cluster(base) abs($demographic)
(MWFE estimator converged in 8 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        634
Absorbing 4 HDFE groups                           F(   8,      4) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.0961
                                                  Adj R-squared   =     0.0384
                                                  Within R-sq.    =     0.0541
Number of clusters (base)    =          5         Root MSE        =     0.2813

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0429949   .0192111    -2.24   0.089    -.0963335    .0103436
             nummask |   .0102826   .0083188     1.24   0.284    -.0128141    .0333792
      ally_relations |   .0448958   .0087051     5.16   0.007     .0207265    .0690652
orientation_officers |  -.0147745   .0086382    -1.71   0.162     -.038758    .0092089
          infocenter |   .0231264   .0220806     1.05   0.354    -.0381793     .084432
         orient_meet |    .088544   .0249697     3.55   0.024     .0192169    .1578711
        war_interest |   -.009359    .012131    -0.77   0.483    -.0430401    .0243221
    honolulu_contact |  -.0003303   .0145393    -0.02   0.983    -.0406979    .0400372
               _cons |   .8889927   .0191729    46.37   0.000     .8357602    .9422252
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est6 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Local "Yes"

added macro:
              e(Local) : "Yes"

.         estadd local Complier "Yes"

added macro:
           e(Complier) : "Yes"

. 
. eststo: reghdfe use numchamber nummask ally_relations orientation_officers infocenter orient_meet war_interest honolulu_contact [pw
> =pr_complier], cluster(base) abs($demographic base)
(MWFE estimator converged in 9 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        634
Absorbing 5 HDFE groups                           F(   8,      4) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.1023
                                                  Adj R-squared   =     0.0369
                                                  Within R-sq.    =     0.0543
Number of clusters (base)    =          5         Root MSE        =     0.2815

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0518827   .0216442    -2.40   0.075    -.1119767    .0082113
             nummask |   .0117379   .0095185     1.23   0.285    -.0146898    .0381656
      ally_relations |   .0467753   .0091746     5.10   0.007     .0213026     .072248
orientation_officers |   -.018413   .0095788    -1.92   0.127     -.045008     .008182
          infocenter |   .0089241    .017686     0.50   0.640    -.0401801    .0580283
         orient_meet |   .0622907    .035187     1.77   0.151    -.0354042    .1599855
        war_interest |  -.0097841   .0113923    -0.86   0.439    -.0414141    .0218459
    honolulu_contact |   -.001159   .0138073    -0.08   0.937    -.0394943    .0371762
               _cons |   .9190466   .0113015    81.32   0.000     .8876686    .9504246
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
          base |         5           5           0    *|
-------------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation
(est7 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Local "Yes"

added macro:
              e(Local) : "Yes"

.         estadd local Base "Yes"

added macro:
               e(Base) : "Yes"

.         estadd local Complier "Yes"

added macro:
           e(Complier) : "Yes"

.         
. esttab using "${result}/CW_Comply.tex", style(tex) b(3) se(3) nonotes keep(numchamber nummask) stats(N aic cov Complier Demog ForRe
> l Officer Info Local Base, fmt(0 0 3 3) labels("Observations" "AIC" "\hline &\\ \textsc{Parameters}" "\hspace{3mm}Pr(Compliance = 1
> )" "\hspace{3mm}Demographics" "\hspace{3mm}Postwar Foreign Policy" "\hspace{3mm}Officers' Leadership" "\hspace{3mm}Information Acce
> ss" "\hspace{3mm}Local Contact" "\hspace{3mm}Airbase FE")) varlabels(numchamber "& \\ Gas Exposure" nummask "& \\ Gas Mask Training
> ") starlevels(* .10 ** .05 *** .01) nolines prehead(\begin{tabular}{l*{9}{c}} \hline & \\ & & \multicolumn{7}{c}{\textbf{Support fo
> r Using Chemical Weapons Against Japan (=1)}} \\ \cline{3-9} & \\) posthead(\hline) prefoot() postfoot(& \\ \hline \end{tabular}) e
> xtracols(1) nomtit replace
(output written to ~/Desktop/JOP Replication/Results/CW_Comply.tex)

. 
. ********************************************************************************
. *                                                                               IPTW                                               
>                 *
. ********************************************************************************
. 
. eststo clear

.         
. eststo: reghdfe use numchamber nummask [pw=iptw], cluster(base) noabs
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =        634
Absorbing 1 HDFE group                            F(   2,      4) =       1.63
Statistics robust to heteroskedasticity           Prob > F        =     0.3036
                                                  R-squared       =     0.0108
                                                  Adj R-squared   =     0.0076
                                                  Within R-sq.    =     0.0108
Number of clusters (base)    =          5         Root MSE        =     0.3241

                                   (Std. Err. adjusted for 5 clusters in base)
------------------------------------------------------------------------------
             |               Robust
         use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  numchamber |  -.0328975   .0190608    -1.73   0.159    -.0858188    .0200237
     nummask |   .0058272   .0133819     0.44   0.686     -.031327    .0429814
       _cons |   .9187529   .0385495    23.83   0.000     .8117223    1.025783
------------------------------------------------------------------------------
(est1 stored)

.         estadd local IPTW "Yes"

added macro:
               e(IPTW) : "Yes"

. 
. eststo: reghdfe use numchamber nummask [pw=iptw], cluster(base) abs($demographic)
(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =        634
Absorbing 4 HDFE groups                           F(   2,      4) =       4.52
Statistics robust to heteroskedasticity           Prob > F        =     0.0942
                                                  R-squared       =     0.1034
                                                  Adj R-squared   =     0.0557
                                                  Within R-sq.    =     0.0092
Number of clusters (base)    =          5         Root MSE        =     0.3162

                                   (Std. Err. adjusted for 5 clusters in base)
------------------------------------------------------------------------------
             |               Robust
         use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  numchamber |  -.0309274   .0117959    -2.62   0.059    -.0636782    .0018234
     nummask |   .0024417   .0082375     0.30   0.782    -.0204294    .0253128
       _cons |   .9222903   .0254776    36.20   0.000     .8515531    .9930275
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est2 stored)

.         estadd local Demog "Yes" 

added macro:
              e(Demog) : "Yes"

.         estadd local IPTW "Yes"

added macro:
               e(IPTW) : "Yes"

. 
. eststo: reghdfe use numchamber nummask ally_relations [pw=iptw], cluster(base) abs($demographic)
(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =        634
Absorbing 4 HDFE groups                           F(   3,      4) =       9.04
Statistics robust to heteroskedasticity           Prob > F        =     0.0296
                                                  R-squared       =     0.1329
                                                  Adj R-squared   =     0.0852
                                                  Within R-sq.    =     0.0418
Number of clusters (base)    =          5         Root MSE        =     0.3112

                                     (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------
               |               Robust
           use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
    numchamber |  -.0301109   .0119296    -2.52   0.065    -.0632327    .0030108
       nummask |   .0022575   .0073026     0.31   0.773    -.0180177    .0225328
ally_relations |   .0605539   .0175236     3.46   0.026     .0119005    .1092073
         _cons |   .9232217   .0234178    39.42   0.000     .8582034      .98824
--------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est3 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local IPTW "Yes"

added macro:
               e(IPTW) : "Yes"

. 
. eststo: reghdfe use numchamber nummask ally_relations orientation_officers [pw=iptw], cluster(base) abs($demographic)
(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =        634
Absorbing 4 HDFE groups                           F(   4,      4) =       7.11
Statistics robust to heteroskedasticity           Prob > F        =     0.0419
                                                  R-squared       =     0.1330
                                                  Adj R-squared   =     0.0838
                                                  Within R-sq.    =     0.0419
Number of clusters (base)    =          5         Root MSE        =     0.3114

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0298952   .0129173    -2.31   0.082    -.0657592    .0059689
             nummask |    .002579   .0068197     0.38   0.725    -.0163557    .0215136
      ally_relations |   .0607501   .0169998     3.57   0.023     .0135512     .107949
orientation_officers |  -.0035653   .0178825    -0.20   0.852    -.0532152    .0460846
               _cons |   .9224084   .0215599    42.78   0.000     .8625485    .9822682
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est4 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local IPTW "Yes"

added macro:
               e(IPTW) : "Yes"

. 
. eststo: reghdfe use numchamber nummask ally_relations orientation_officers infocenter orient_meet war_interest [pw=iptw], cluster(b
> ase) abs($demographic)
(MWFE estimator converged in 8 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        634
Absorbing 4 HDFE groups                           F(   7,      4) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.1831
                                                  Adj R-squared   =     0.1324
                                                  Within R-sq.    =     0.0973
Number of clusters (base)    =          5         Root MSE        =     0.3031

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0341343   .0148251    -2.30   0.083    -.0752954    .0070267
             nummask |  -.0019712   .0078404    -0.25   0.814    -.0237396    .0197973
      ally_relations |   .0638995   .0180412     3.54   0.024     .0138092    .1139898
orientation_officers |  -.0272701   .0183583    -1.49   0.212    -.0782409    .0237006
          infocenter |   .0970211   .0483862     2.01   0.115    -.0373207    .2313628
         orient_meet |   .0981334   .0372876     2.63   0.058    -.0053935    .2016603
        war_interest |   .0087101    .018451     0.47   0.661    -.0425182    .0599384
               _cons |    .832985   .0423397    19.67   0.000     .7154311    .9505388
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est5 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local IPTW "Yes"

added macro:
               e(IPTW) : "Yes"

. 
. eststo: reghdfe use numchamber nummask ally_relations orientation_officers infocenter orient_meet war_interest honolulu_contact [pw
> =iptw], cluster(base) abs($demographic)
(MWFE estimator converged in 8 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        634
Absorbing 4 HDFE groups                           F(   8,      4) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.1832
                                                  Adj R-squared   =     0.1311
                                                  Within R-sq.    =     0.0974
Number of clusters (base)    =          5         Root MSE        =     0.3033

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0340525   .0150386    -2.26   0.086    -.0758062    .0077013
             nummask |  -.0018149   .0079946    -0.23   0.832    -.0240114    .0203816
      ally_relations |   .0636205   .0187454     3.39   0.027     .0115748    .1156662
orientation_officers |  -.0277311   .0196367    -1.41   0.231    -.0822514    .0267892
          infocenter |   .0968812   .0486518     1.99   0.117     -.038198    .2319603
         orient_meet |   .0981169   .0372963     2.63   0.058    -.0054343    .2016682
        war_interest |   .0086017   .0183267     0.47   0.663    -.0422814    .0594847
    honolulu_contact |    .003561   .0168612     0.21   0.843    -.0432533    .0503753
               _cons |   .8327118   .0422465    19.71   0.000     .7154166     .950007
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est6 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Local "Yes"

added macro:
              e(Local) : "Yes"

.         estadd local IPTW "Yes"

added macro:
               e(IPTW) : "Yes"

. 
. eststo: reghdfe use numchamber nummask ally_relations orientation_officers infocenter orient_meet war_interest honolulu_contact [pw
> =iptw], cluster(base) abs($demographic base)
(MWFE estimator converged in 9 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        634
Absorbing 5 HDFE groups                           F(   8,      4) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.1982
                                                  Adj R-squared   =     0.1397
                                                  Within R-sq.    =     0.0774
Number of clusters (base)    =          5         Root MSE        =     0.3018

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0386653   .0153975    -2.51   0.066    -.0814156     .004085
             nummask |  -.0052938   .0087988    -0.60   0.580     -.029723    .0191355
      ally_relations |   .0648598   .0189023     3.43   0.027     .0123787    .1173409
orientation_officers |  -.0330723   .0207847    -1.59   0.187    -.0907799    .0246353
          infocenter |   .0710841   .0549892     1.29   0.266    -.0815904    .2237586
         orient_meet |    .071945   .0480788     1.50   0.209    -.0615433    .2054333
        war_interest |   .0043289   .0171801     0.25   0.813    -.0433706    .0520284
    honolulu_contact |   .0000334     .01687     0.00   0.999    -.0468052    .0468721
               _cons |   .8747166   .0387953    22.55   0.000     .7670036    .9824295
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
          base |         5           5           0    *|
-------------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation
(est7 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Local "Yes"

added macro:
              e(Local) : "Yes"

.         estadd local Base "Yes"

added macro:
               e(Base) : "Yes"

.         estadd local IPTW "Yes"

added macro:
               e(IPTW) : "Yes"

.         
. esttab using "${result}/CW_IPTW.tex", style(tex) b(3) se(3) nonotes keep(numchamber nummask) stats(N aic cov IPTW Demog ForRel Offi
> cer Info Local Base, fmt(0 0 3 3) labels("Observations" "AIC" "\hline &\\ \textsc{Parameters}" "\hspace{3mm}IPTW" "\hspace{3mm}Demo
> graphics" "\hspace{3mm}Postwar Foreign Policy" "\hspace{3mm}Officers' Leadership" "\hspace{3mm}Information Access" "\hspace{3mm}Loc
> al Contact" "\hspace{3mm}Airbase FE")) varlabels(numchamber "& \\ Gas Exposure" nummask "& \\ Gas Mask Training") starlevels(* .10 
> ** .05 *** .01) nolines prehead(\begin{tabular}{l*{9}{c}} \hline & \\ & & \multicolumn{7}{c}{\textbf{Support for Using Chemical Wea
> pons Against Japan (=1)}} \\ \cline{3-9} & \\) posthead(\hline) prefoot() postfoot(& \\ \hline \end{tabular}) extracols(1) nomtit r
> eplace
(output written to ~/Desktop/JOP Replication/Results/CW_IPTW.tex)

. 
. ********************************************************************************
. *                                                                               CEM                                                
>                         *
. ********************************************************************************
. 
. eststo clear

.         
. eststo: reghdfe use numchamber nummask [pw=cem_weights1] if cem_matched1, cluster(base) noabs
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =        634
Absorbing 1 HDFE group                            F(   2,      4) =       5.19
Statistics robust to heteroskedasticity           Prob > F        =     0.0775
                                                  R-squared       =     0.0067
                                                  Adj R-squared   =     0.0035
                                                  Within R-sq.    =     0.0067
Number of clusters (base)    =          5         Root MSE        =     0.2876

                                   (Std. Err. adjusted for 5 clusters in base)
------------------------------------------------------------------------------
             |               Robust
         use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  numchamber |  -.0208022   .0081009    -2.57   0.062    -.0432939    .0016896
     nummask |   .0072104   .0054223     1.33   0.254    -.0078443     .022265
       _cons |   .9186052   .0096624    95.07   0.000      .891778    .9454323
------------------------------------------------------------------------------
(est1 stored)

.         estadd local CEM "Yes"

added macro:
                e(CEM) : "Yes"

. 
. eststo: reghdfe use numchamber nummask [pw=cem_weights1] if cem_matched1, cluster(base) abs($demographic)
(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =        634
Absorbing 4 HDFE groups                           F(   2,      4) =      10.64
Statistics robust to heteroskedasticity           Prob > F        =     0.0251
                                                  R-squared       =     0.0493
                                                  Adj R-squared   =    -0.0013
                                                  Within R-sq.    =     0.0049
Number of clusters (base)    =          5         Root MSE        =     0.2883

                                   (Std. Err. adjusted for 5 clusters in base)
------------------------------------------------------------------------------
             |               Robust
         use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  numchamber |  -.0199552   .0050442    -3.96   0.017    -.0339602   -.0059502
     nummask |   .0049252   .0065771     0.75   0.496    -.0133357    .0231862
       _cons |   .9222975   .0180457    51.11   0.000     .8721945    .9724004
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est2 stored)

.         estadd local CEM "Yes"

added macro:
                e(CEM) : "Yes"

.         estadd local Demog "Yes" 

added macro:
              e(Demog) : "Yes"

. 
. eststo: reghdfe use numchamber nummask ally_relations [pw=cem_weights1] if cem_matched1, cluster(base) abs($demographic)
(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =        634
Absorbing 4 HDFE groups                           F(   3,      4) =     331.64
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.0695
                                                  Adj R-squared   =     0.0183
                                                  Within R-sq.    =     0.0260
Number of clusters (base)    =          5         Root MSE        =     0.2855

                                     (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------
               |               Robust
           use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
    numchamber |  -.0184571   .0033967    -5.43   0.006     -.027888   -.0090263
       nummask |   .0051102    .006641     0.77   0.485    -.0133281    .0235485
ally_relations |   .0431203   .0069303     6.22   0.003     .0238789    .0623618
         _cons |   .9203068   .0197303    46.64   0.000     .8655268    .9750867
--------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est3 stored)

.         estadd local CEM "Yes"

added macro:
                e(CEM) : "Yes"

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

. 
. eststo: reghdfe use numchamber nummask ally_relations orientation_officers [pw=cem_weights1] if cem_matched1, cluster(base) abs($de
> mographic)
(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =        634
Absorbing 4 HDFE groups                           F(   4,      4) =    1299.48
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.0708
                                                  Adj R-squared   =     0.0180
                                                  Within R-sq.    =     0.0273
Number of clusters (base)    =          5         Root MSE        =     0.2855

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0160856   .0050396    -3.19   0.033    -.0300779   -.0020934
             nummask |   .0060688   .0061936     0.98   0.383    -.0111273     .023265
      ally_relations |   .0442108   .0069317     6.38   0.003     .0249655    .0634562
orientation_officers |  -.0109007    .009805    -1.11   0.329    -.0381237    .0163224
               _cons |    .915717   .0194295    47.13   0.000     .8617722    .9696619
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est4 stored)

.         estadd local CEM "Yes"

added macro:
                e(CEM) : "Yes"

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

. 
. eststo: reghdfe use numchamber nummask ally_relations orientation_officers infocenter orient_meet war_interest [pw=cem_weights1] if
>  cem_matched1, cluster(base) abs($demographic)
(MWFE estimator converged in 8 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        634
Absorbing 4 HDFE groups                           F(   7,      4) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.0814
                                                  Adj R-squared   =     0.0244
                                                  Within R-sq.    =     0.0385
Number of clusters (base)    =          5         Root MSE        =     0.2846

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0284684   .0070331    -4.05   0.016    -.0479954   -.0089413
             nummask |   .0045999   .0055935     0.82   0.457      -.01093    .0201299
      ally_relations |   .0445596   .0074334     5.99   0.004     .0239213    .0651979
orientation_officers |  -.0190849   .0139673    -1.37   0.244    -.0578644    .0196946
          infocenter |   .0194743   .0295648     0.66   0.546    -.0626106    .1015592
         orient_meet |   .0611962   .0282125     2.17   0.096    -.0171344    .1395267
        war_interest |   .0030921   .0156426     0.20   0.853    -.0403388     .046523
               _cons |   .8894721   .0197663    45.00   0.000      .834592    .9443523
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est5 stored)

.         estadd local CEM "Yes"

added macro:
                e(CEM) : "Yes"

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

. 
. eststo: reghdfe use numchamber nummask ally_relations orientation_officers infocenter orient_meet war_interest honolulu_contact [pw
> =cem_weights1] if cem_matched1, cluster(base) abs($demographic)
(MWFE estimator converged in 8 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        634
Absorbing 4 HDFE groups                           F(   8,      4) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.0828
                                                  Adj R-squared   =     0.0242
                                                  Within R-sq.    =     0.0399
Number of clusters (base)    =          5         Root MSE        =     0.2846

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0281735   .0066527    -4.23   0.013    -.0466443   -.0097026
             nummask |   .0049895   .0054058     0.92   0.408    -.0100195    .0199984
      ally_relations |   .0436847   .0081688     5.35   0.006     .0210046    .0663647
orientation_officers |  -.0199574   .0142005    -1.41   0.233    -.0593842    .0194695
          infocenter |   .0179414   .0291756     0.61   0.572     -.063063    .0989458
         orient_meet |   .0619515   .0276963     2.24   0.089    -.0149456    .1388487
        war_interest |    .002496   .0160199     0.16   0.884    -.0419823    .0469743
    honolulu_contact |   .0112778   .0153649     0.73   0.504    -.0313819    .0539375
               _cons |   .8888246   .0194627    45.67   0.000     .8347875    .9428618
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est6 stored)

.         estadd local CEM "Yes"

added macro:
                e(CEM) : "Yes"

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Local "Yes"

added macro:
              e(Local) : "Yes"

. 
. eststo: reghdfe use numchamber nummask ally_relations orientation_officers infocenter orient_meet war_interest honolulu_contact [pw
> =cem_weights1] if cem_matched1, cluster(base) abs($demographic base)
(MWFE estimator converged in 9 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        634
Absorbing 5 HDFE groups                           F(   8,      4) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.0855
                                                  Adj R-squared   =     0.0189
                                                  Within R-sq.    =     0.0386
Number of clusters (base)    =          5         Root MSE        =     0.2854

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0275986   .0059378    -4.65   0.010    -.0440845   -.0111128
             nummask |   .0030488   .0064414     0.47   0.661    -.0148352    .0209329
      ally_relations |   .0442763    .008411     5.26   0.006     .0209236     .067629
orientation_officers |  -.0220567   .0156041    -1.41   0.230    -.0653805    .0212671
          infocenter |   .0125268   .0318212     0.39   0.714    -.0758231    .1008766
         orient_meet |   .0591139    .032398     1.82   0.142    -.0308374    .1490652
        war_interest |   .0017466   .0159462     0.11   0.918    -.0425272    .0460204
    honolulu_contact |   .0110267   .0151063     0.73   0.506    -.0309151    .0529684
               _cons |    .896729   .0180655    49.64   0.000     .8465712    .9468867
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
          base |         5           5           0    *|
-------------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation
(est7 stored)

.         estadd local CEM "Yes"

added macro:
                e(CEM) : "Yes"

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Local "Yes"

added macro:
              e(Local) : "Yes"

.         estadd local Base "Yes"

added macro:
               e(Base) : "Yes"

.         
. esttab using "${result}/CW_CEM1.tex", style(tex) b(3) se(3) nonotes keep(numchamber nummask) stats(N aic, fmt(0 0 3 3) labels("Obse
> rvations" "AIC")) varlabels(numchamber "& \\ Gas Exposure" nummask "& \\ Gas Mask Training") starlevels(* .10 ** .05 *** .01) nolin
> es prehead(\begin{tabular}{l*{9}{c}} \hline & \\ \textbf{Panel A:} & & \multicolumn{7}{c}{\textbf{Support for Using Chemical Weapon
> s Against Japan (=1)}} \\ \cline{3-9} & \\) posthead(\hline) prefoot() postfoot(& \\ \hline \end{tabular}) extracols(1) nomtit repl
> ace
(output written to ~/Desktop/JOP Replication/Results/CW_CEM1.tex)

. 
. eststo clear

.         
. eststo: reghdfe use numchamber nummask [pw=cem_weights2] if cem_matched2, cluster(base) noabs
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =        634
Absorbing 1 HDFE group                            F(   2,      4) =       1.96
Statistics robust to heteroskedasticity           Prob > F        =     0.2547
                                                  R-squared       =     0.0056
                                                  Adj R-squared   =     0.0025
                                                  Within R-sq.    =     0.0056
Number of clusters (base)    =          5         Root MSE        =     0.2902

                                   (Std. Err. adjusted for 5 clusters in base)
------------------------------------------------------------------------------
             |               Robust
         use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  numchamber |  -.0186134   .0096363    -1.93   0.126    -.0453681    .0081413
     nummask |   .0071018   .0059428     1.20   0.298     -.009398    .0236016
       _cons |   .9145178   .0117339    77.94   0.000     .8819394    .9470962
------------------------------------------------------------------------------
(est1 stored)

.         estadd local CEM "Yes"

added macro:
                e(CEM) : "Yes"

. 
. eststo: reghdfe use numchamber nummask [pw=cem_weights2] if cem_matched2, cluster(base) abs($demographic)
(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =        634
Absorbing 4 HDFE groups                           F(   2,      4) =       4.57
Statistics robust to heteroskedasticity           Prob > F        =     0.0927
                                                  R-squared       =     0.0565
                                                  Adj R-squared   =     0.0062
                                                  Within R-sq.    =     0.0040
Number of clusters (base)    =          5         Root MSE        =     0.2897

                                   (Std. Err. adjusted for 5 clusters in base)
------------------------------------------------------------------------------
             |               Robust
         use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  numchamber |  -.0181951   .0064125    -2.84   0.047    -.0359991   -.0003911
     nummask |   .0043541   .0070381     0.62   0.570    -.0151868    .0238949
       _cons |   .9196665   .0194831    47.20   0.000     .8655729    .9737602
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est2 stored)

.         estadd local CEM "Yes"

added macro:
                e(CEM) : "Yes"

.         estadd local Demog "Yes" 

added macro:
              e(Demog) : "Yes"

. 
. eststo: reghdfe use numchamber nummask ally_relations [pw=cem_weights2] if cem_matched2, cluster(base) abs($demographic)
(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =        634
Absorbing 4 HDFE groups                           F(   3,      4) =      78.67
Statistics robust to heteroskedasticity           Prob > F        =     0.0005
                                                  R-squared       =     0.0780
                                                  Adj R-squared   =     0.0272
                                                  Within R-sq.    =     0.0267
Number of clusters (base)    =          5         Root MSE        =     0.2866

                                     (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------
               |               Robust
           use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
    numchamber |  -.0166065   .0046581    -3.57   0.023    -.0295395   -.0036734
       nummask |   .0044407    .006975     0.64   0.559     -.014925    .0238064
ally_relations |   .0453068   .0085106     5.32   0.006     .0216777    .0689359
         _cons |   .9180343   .0204639    44.86   0.000     .8612173    .9748512
--------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est3 stored)

.         estadd local CEM "Yes"

added macro:
                e(CEM) : "Yes"

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

. 
. eststo: reghdfe use numchamber nummask ally_relations orientation_officers [pw=cem_weights2] if cem_matched2, cluster(base) abs($de
> mographic)
(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =        634
Absorbing 4 HDFE groups                           F(   4,      4) =     787.60
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.0813
                                                  Adj R-squared   =     0.0292
                                                  Within R-sq.    =     0.0303
Number of clusters (base)    =          5         Root MSE        =     0.2863

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0125816   .0066867    -1.88   0.133    -.0311468    .0059837
             nummask |   .0060028   .0065882     0.91   0.414     -.012289    .0242947
      ally_relations |   .0469981   .0084872     5.54   0.005     .0234339    .0705623
orientation_officers |  -.0181493   .0105153    -1.73   0.159    -.0473444    .0110458
               _cons |   .9103738   .0214452    42.45   0.000     .8508323    .9699153
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est4 stored)

.         estadd local CEM "Yes"

added macro:
                e(CEM) : "Yes"

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

. 
. eststo: reghdfe use numchamber nummask ally_relations orientation_officers infocenter orient_meet war_interest [pw=cem_weights2] if
>  cem_matched2, cluster(base) abs($demographic)
(MWFE estimator converged in 8 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        634
Absorbing 4 HDFE groups                           F(   7,      4) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.0938
                                                  Adj R-squared   =     0.0376
                                                  Within R-sq.    =     0.0434
Number of clusters (base)    =          5         Root MSE        =     0.2851

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0261478   .0089195    -2.93   0.043    -.0509124   -.0013831
             nummask |   .0040993   .0059452     0.69   0.528    -.0124072    .0206058
      ally_relations |   .0473175   .0092577     5.11   0.007     .0216141     .073021
orientation_officers |  -.0269082   .0146432    -1.84   0.140    -.0675643    .0137479
          infocenter |   .0260808    .030919     0.84   0.446     -.059764    .1119257
         orient_meet |   .0637686    .029768     2.14   0.099    -.0188804    .1464177
        war_interest |   .0033435   .0162192     0.21   0.847    -.0416883    .0483752
               _cons |    .881435   .0187508    47.01   0.000     .8293746    .9334955
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est5 stored)

.         estadd local CEM "Yes"

added macro:
                e(CEM) : "Yes"

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

. 
. eststo: reghdfe use numchamber nummask ally_relations orientation_officers infocenter orient_meet war_interest honolulu_contact [pw
> =cem_weights2] if cem_matched2, cluster(base) abs($demographic)
(MWFE estimator converged in 8 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        634
Absorbing 4 HDFE groups                           F(   8,      4) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.0957
                                                  Adj R-squared   =     0.0380
                                                  Within R-sq.    =     0.0455
Number of clusters (base)    =          5         Root MSE        =     0.2850

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |   -.025645   .0083816    -3.06   0.038    -.0489159   -.0023741
             nummask |   .0045933   .0057095     0.80   0.466    -.0112587    .0204454
      ally_relations |   .0464107   .0098713     4.70   0.009     .0190037    .0738178
orientation_officers |  -.0279501   .0149554    -1.87   0.135     -.069473    .0135727
          infocenter |   .0241447   .0301771     0.80   0.468    -.0596402    .1079297
         orient_meet |   .0640876   .0298729     2.15   0.098    -.0188529    .1470282
        war_interest |   .0027722   .0165821     0.17   0.875    -.0432671    .0488116
    honolulu_contact |   .0135815   .0144696     0.94   0.401    -.0265924    .0537555
               _cons |   .8806996   .0183968    47.87   0.000     .8296218    .9317773
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est6 stored)

.         estadd local CEM "Yes"

added macro:
                e(CEM) : "Yes"

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Local "Yes"

added macro:
              e(Local) : "Yes"

. 
. eststo: reghdfe use numchamber nummask ally_relations orientation_officers infocenter orient_meet war_interest honolulu_contact [pw
> =cem_weights2] if cem_matched2, cluster(base) abs($demographic base)
(MWFE estimator converged in 9 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        634
Absorbing 5 HDFE groups                           F(   8,      4) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.0996
                                                  Adj R-squared   =     0.0340
                                                  Within R-sq.    =     0.0446
Number of clusters (base)    =          5         Root MSE        =     0.2856

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0252534   .0075417    -3.35   0.029    -.0461924   -.0043144
             nummask |   .0022396   .0068423     0.33   0.760    -.0167576    .0212368
      ally_relations |   .0472263   .0102831     4.59   0.010     .0186759    .0757767
orientation_officers |  -.0305888    .016596    -1.84   0.139    -.0766666     .015489
          infocenter |   .0181078   .0341812     0.53   0.624    -.0767944      .11301
         orient_meet |   .0610363   .0333134     1.83   0.141    -.0314564     .153529
        war_interest |    .001812   .0166262     0.11   0.918    -.0443498    .0479737
    honolulu_contact |    .012977   .0142216     0.91   0.413    -.0265085    .0524624
               _cons |   .8901073   .0152116    58.52   0.000     .8478732    .9323413
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
          base |         5           5           0    *|
-------------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation
(est7 stored)

.         estadd local CEM "Yes"

added macro:
                e(CEM) : "Yes"

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Local "Yes"

added macro:
              e(Local) : "Yes"

.         estadd local Base "Yes"

added macro:
               e(Base) : "Yes"

.         
. esttab using "${result}/CW_CEM2.tex", style(tex) b(3) se(3) nonotes keep(numchamber nummask) stats(N aic cov CEM Demog ForRel Offic
> er Info Local Base, fmt(0 0 3 3) labels("Observations" "AIC" "\hline &\\ \textsc{Parameters}" "\hspace{3mm}CEM" "\hspace{3mm}Demogr
> aphics" "\hspace{3mm}Postwar Foreign Policy" "\hspace{3mm}Officers' Leadership" "\hspace{3mm}Information Access" "\hspace{3mm}Local
>  Contact" "\hspace{3mm}Airbase FE")) varlabels(numchamber "& \\ Gas Exposure" nummask "& \\ Gas Mask Training") starlevels(* .10 **
>  .05 *** .01) nolines prehead(\begin{tabular}{l*{9}{c}} \hline & \\ \textbf{Panel B:} & & \multicolumn{7}{c}{\textbf{Support for Us
> ing Chemical Weapons Against Japan (=1)}} \\ \cline{3-9} & \\) posthead(\hline) prefoot() postfoot(& \\ \hline \end{tabular}) extra
> cols(1) nomtit replace
(output written to ~/Desktop/JOP Replication/Results/CW_CEM2.tex)

. 
. ********************************************************************************
. *                                                                               CMP                                                
>                         *
. ********************************************************************************
. 
. eststo clear

. 
. cmp setup
$cmp_out      = 0
$cmp_missing  = .
$cmp_cont     = 1
$cmp_left     = 2
$cmp_right    = 3
$cmp_probit   = 4
$cmp_oprobit  = 5
$cmp_mprobit  = 6
$cmp_int      = 7
$cmp_trunc    = 8  (deprecated)
$cmp_roprobit = 9
$cmp_frac     = 10

. 
. eststo: cmp (use_comply= numchamber nummask) (complier = i.($demographic base) training orientation_officers), ind($cmp_cont $cmp_c
> ont) cluster(base)

Fitting individual models as starting point for full model fit.
Note: For programming reasons, these initial estimates may deviate from your specification.
      For exact fits of each equation alone, run cmp separately on each.

      Source |       SS           df       MS      Number of obs   =       334
-------------+----------------------------------   F(2, 331)       =      1.95
       Model |  .317518781         2  .158759391   Prob > F        =    0.1443
    Residual |  26.9878704       331  .081534352   R-squared       =    0.0116
-------------+----------------------------------   Adj R-squared   =    0.0057
       Total |  27.3053892       333  .081998166   Root MSE        =    .28554

------------------------------------------------------------------------------
  use_comply |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  numchamber |   -.020763   .0172024    -1.21   0.228    -.0546027    .0130767
     nummask |   .0097466   .0092746     1.05   0.294    -.0084981    .0279912
       _cons |   .9147784   .0359195    25.47   0.000     .8441192    .9854376
------------------------------------------------------------------------------

      Source |       SS           df       MS      Number of obs   =       648
-------------+----------------------------------   F(36, 611)      =     17.20
       Model |  81.2612191        36  2.25725609   Prob > F        =    0.0000
    Residual |  80.1816821       611  .131230249   R-squared       =    0.5033
-------------+----------------------------------   Adj R-squared   =    0.4741
       Total |  161.442901       647   .24952535   Root MSE        =    .36226

--------------------------------------------------------------------------------------
            complier |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
                 age |
         18 or Less  |  -.0580267    .314057    -0.18   0.853    -.6747889    .5587354
                 19  |   .2517184   .2041668     1.23   0.218    -.1492354    .6526723
                 20  |   .1845914   .1807758     1.02   0.308    -.1704259    .5396087
                 21  |   .0482339   .1811139     0.27   0.790    -.3074474    .4039152
                 22  |   .2349914   .1772936     1.33   0.186    -.1131872    .5831701
                 23  |   .1290968   .1788322     0.72   0.471    -.2221035    .4802972
                 24  |   .2044587    .177114     1.15   0.249    -.1433674    .5522848
                 25  |    .109656   .1802608     0.61   0.543      -.24435     .463662
              26-29  |   .1440453   .1742154     0.83   0.409    -.1980884    .4861789
              30-34  |   .1423899   .1757027     0.81   0.418    -.2026645    .4874443
                35+  |    .158674   .1761656     0.90   0.368    -.1872896    .5046375
                     |
              school |
         <8th Grade  |  -.2679469   .2302047    -1.16   0.245    -.7200354    .1841416
          8th Grade  |  -.2449809   .2298372    -1.07   0.287    -.6963477    .2063858
   Some High School  |  -.1919939   .2283839    -0.84   0.401    -.6405066    .2565188
        High School  |  -.2075978   .2286571    -0.91   0.364     -.656647    .2414514
            College  |  -.1978111   .2311899    -0.86   0.393    -.6518344    .2562122
                     |
       monthsoversea |
   3 Months or Less  |   .1206629   .2179681     0.55   0.580    -.3073947    .5487205
         3-6 Months  |   .2178059   .2211842     0.98   0.325    -.2165676    .6521794
         6-9 Months  |    .035603   .2259656     0.16   0.875    -.4081606    .4793665
        9-12 Months  |   .2254623   .2158898     1.04   0.297    -.1985139    .6494384
       12-18 Months  |   .1125408    .215451     0.52   0.602    -.3105735    .5356551
       18-24 Months  |   .0477386   .2141347     0.22   0.824    -.3727908     .468268
       24-30 Months  |    .059309   .2134854     0.28   0.781    -.3599452    .4785632
       30-36 Months  |   .0692846   .2285666     0.30   0.762     -.379587    .5181561
         36+ Months  |   .1164172   .2174203     0.54   0.593    -.3105646     .543399
                     |
           rankgrade |
       PRV. or PFC.  |   .0528193   .1361178     0.39   0.698    -.2144962    .3201347
        CPL or TCH5  |   .0678775   .1369715     0.50   0.620    -.2011144    .3368695
        SGT or TCH4  |   .0446596   .1384256     0.32   0.747    -.2271882    .3165074
       SSGT or TCH3  |  -.1255978   .1444415    -0.87   0.385    -.4092598    .1580642
   TSGT, MSGT, 1SGT  |  -.0926349   .1557689    -0.59   0.552    -.3985424    .2132726
                     |
                base |
            Wheeler  |   .0995622   .0563472     1.77   0.078    -.0110954    .2102198
           Mokuleia  |  -.2207373   .0609901    -3.62   0.000    -.3405129   -.1009617
            Bellows  |  -.0532516   .0432227    -1.23   0.218    -.1381346    .0316315
             Kahulu  |   .1709229   .0456701     3.74   0.000     .0812334    .2606124
                     |
            training |   .7187303   .0352632    20.38   0.000     .6494786    .7879821
orientation_officers |    .029198   .0154934     1.88   0.060    -.0012288    .0596249
               _cons |   .1062798   .3383179     0.31   0.754    -.5581272    .7706868
--------------------------------------------------------------------------------------

Warning: regressor matrix for complier equation appears ill-conditioned. (Condition number = 1425.2195.)
This might prevent convergence. If it does, and if you have not done so already, you may need to remove nearly
collinear regressors to achieve convergence. Or you may need to add a nrtolerance(#) or nonrtolerance option to the command line.
See cmp tips.

Fitting full model.

Iteration 0:   log pseudolikelihood = -296.72426  
Iteration 1:   log pseudolikelihood =  -296.0027  
Iteration 2:   log pseudolikelihood = -296.00109  
Iteration 3:   log pseudolikelihood = -296.00109  

Mixed-process regression                        Number of obs     =        648
                                                Wald chi2(4)      =       8.67
Log pseudolikelihood = -296.00109               Prob > chi2       =     0.0700

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                     |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
use_comply           |
          numchamber |  -.0294113   .0205952    -1.43   0.153     -.069777    .0109545
             nummask |   .0103627   .0087066     1.19   0.234    -.0067018    .0274272
               _cons |   .9358406   .0328583    28.48   0.000     .8714395    1.000242
---------------------+----------------------------------------------------------------
complier             |
                 age |
         18 or Less  |  -.0567326   .1967906    -0.29   0.773    -.4424351    .3289699
                 19  |   .2489507   .1565829     1.59   0.112    -.0579463    .5558476
                 20  |   .1858746   .1319096     1.41   0.159    -.0726634    .4444125
                 21  |   .0490248   .0913733     0.54   0.592    -.1300636    .2281132
                 22  |    .231044   .1018519     2.27   0.023      .031418      .43067
                 23  |   .1327196   .0685154     1.94   0.053    -.0015682    .2670073
                 24  |   .2065468   .1130431     1.83   0.068    -.0150136    .4281072
                 25  |    .110037   .0892135     1.23   0.217    -.0648183    .2848923
              26-29  |   .1462572   .0987238     1.48   0.138    -.0472378    .3397522
              30-34  |   .1444841   .1022795     1.41   0.158    -.0559801    .3449483
                35+  |    .159214   .1170799     1.36   0.174    -.0702583    .3886863
                     |
              school |
         <8th Grade  |  -.2712199   .0635842    -4.27   0.000    -.3958427    -.146597
          8th Grade  |  -.2507365   .0772451    -3.25   0.001    -.4021341   -.0993389
   Some High School  |  -.1976821   .0555847    -3.56   0.000     -.306626   -.0887381
        High School  |  -.2102397   .0830889    -2.53   0.011    -.3730909   -.0473886
            College  |  -.2023624    .121982    -1.66   0.097    -.4414428     .036718
                     |
       monthsoversea |
   3 Months or Less  |   .1212506   .0859145     1.41   0.158    -.0471386    .2896399
         3-6 Months  |   .2105199   .1390445     1.51   0.130    -.0620023     .483042
         6-9 Months  |   .0369615   .1237696     0.30   0.765    -.2056225    .2795456
        9-12 Months  |   .2266855   .0924508     2.45   0.014     .0454852    .4078858
       12-18 Months  |   .1123045   .1363556     0.82   0.410    -.1549475    .3795566
       18-24 Months  |   .0476249   .0846832     0.56   0.574    -.1183512     .213601
       24-30 Months  |    .059064   .1156365     0.51   0.610    -.1675794    .2857073
       30-36 Months  |   .0724404    .135226     0.54   0.592    -.1925977    .3374786
         36+ Months  |   .1179295   .1497129     0.79   0.431    -.1755025    .4113614
                     |
           rankgrade |
       PRV. or PFC.  |   .0634726   .1082637     0.59   0.558    -.1487203    .2756655
        CPL or TCH5  |   .0778575   .1201501     0.65   0.517    -.1576322    .3133473
        SGT or TCH4  |   .0532391   .1209329     0.44   0.660    -.1837852    .2902633
       SSGT or TCH3  |  -.1131328   .0945644    -1.20   0.232    -.2984756      .07221
   TSGT, MSGT, 1SGT  |  -.0863177   .0706275    -1.22   0.222    -.2247451    .0521098
                     |
                base |
            Wheeler  |   .0961617   .0285664     3.37   0.001     .0401726    .1521507
           Mokuleia  |  -.2287151   .0774614    -2.95   0.003    -.3805367   -.0768935
            Bellows  |  -.0546128   .0226838    -2.41   0.016    -.0990721   -.0101534
             Kahulu  |   .1678131   .0203507     8.25   0.000     .1279265    .2076998
                     |
            training |   .7210478   .0994527     7.25   0.000     .5261241    .9159715
orientation_officers |     .02888   .0235281     1.23   0.220    -.0172342    .0749942
               _cons |    .100739   .2310852     0.44   0.663    -.3521796    .5536577
---------------------+----------------------------------------------------------------
            /lnsig_1 |  -1.256632   .1015119   -12.38   0.000    -1.455592   -1.057672
            /lnsig_2 |  -1.044739   .1225081    -8.53   0.000    -1.284851   -.8046277
        /atanhrho_12 |  -.0643274   .0870037    -0.74   0.460    -.2348514    .1061967
---------------------+----------------------------------------------------------------
               sig_1 |    .284611   .0288914                      .2332623    .3472633
               sig_2 |   .3517836   .0430963                      .2766919    .4472544
              rho_12 |  -.0642388   .0866446                     -.2306268    .1057993
--------------------------------------------------------------------------------------
(est1 stored)

. 
. eststo: cmp (use_comply= numchamber nummask i.($demographic)) (complier = i.($demographic base) training orientation_officers), ind
> ($cmp_cont $cmp_cont) cluster(base)

Fitting individual models as starting point for full model fit.
Note: For programming reasons, these initial estimates may deviate from your specification.
      For exact fits of each equation alone, run cmp separately on each.

      Source |       SS           df       MS      Number of obs   =       334
-------------+----------------------------------   F(32, 301)      =      0.86
       Model |  2.28740775        32  .071481492   Prob > F        =    0.6878
    Residual |  25.0179815       301  .083116218   R-squared       =    0.0838
-------------+----------------------------------   Adj R-squared   =   -0.0136
       Total |  27.3053892       333  .081998166   Root MSE        =     .2883

-----------------------------------------------------------------------------------
       use_comply |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
       numchamber |  -.0192289   .0192711    -1.00   0.319     -.057152    .0186943
          nummask |   .0100647   .0103383     0.97   0.331    -.0102798    .0304093
                  |
              age |
      18 or Less  |  -.1185082    .415332    -0.29   0.776    -.9358303    .6988139
              19  |  -.2572564   .3154896    -0.82   0.415    -.8781009    .3635882
              20  |   -.142306   .2998281    -0.47   0.635    -.7323307    .4477187
              21  |  -.1613812   .3003521    -0.54   0.591    -.7524371    .4296747
              22  |   -.261992   .2972018    -0.88   0.379    -.8468484    .3228644
              23  |  -.1080935   .2998964    -0.36   0.719    -.6982526    .4820655
              24  |  -.1332172   .2983246    -0.45   0.656    -.7202831    .4538487
              25  |  -.1644641    .302569    -0.54   0.587    -.7598824    .4309542
           26-29  |  -.1216878   .2972022    -0.41   0.683     -.706545    .4631694
           30-34  |  -.1207345   .2963932    -0.41   0.684    -.7039998    .4625308
             35+  |   -.155846    .298059    -0.52   0.601    -.7423893    .4306974
                  |
           school |
      <8th Grade  |  -.1327109   .3046596    -0.44   0.663    -.7322434    .4668217
       8th Grade  |  -.1936945   .3025236    -0.64   0.522    -.7890236    .4016345
Some High School  |   -.182643   .3004969    -0.61   0.544    -.7739837    .4086977
     High School  |  -.1086477   .3013914    -0.36   0.719    -.7017488    .4844534
         College  |  -.1630158   .3044632    -0.54   0.593    -.7621618    .4361302
                  |
    monthsoversea |
3 Months or Less  |  -.0602596   .3054488    -0.20   0.844     -.661345    .5408258
      3-6 Months  |  -.2095234   .3034622    -0.69   0.490    -.8066996    .3876528
      6-9 Months  |  -.0903633   .3089393    -0.29   0.770    -.6983176    .5175911
     9-12 Months  |  -.0669285   .3000296    -0.22   0.824    -.6573497    .5234928
    12-18 Months  |  -.1040097   .3008448    -0.35   0.730    -.6960351    .4880157
    18-24 Months  |  -.0891243   .2992422    -0.30   0.766    -.6779961    .4997475
    24-30 Months  |  -.0971992   .2996735    -0.32   0.746    -.6869196    .4925213
    30-36 Months  |   .0108939   .3252909     0.03   0.973    -.6292384    .6510261
      36+ Months  |  -.0226556   .3041751    -0.07   0.941    -.6212347    .5759235
                  |
        rankgrade |
    PRV. or PFC.  |   .2140688   .1533347     1.40   0.164    -.0876751    .5158126
     CPL or TCH5  |   .1922069   .1530856     1.26   0.210    -.1090467    .4934605
     SGT or TCH4  |   .1491012   .1542052     0.97   0.334    -.1543556     .452558
    SSGT or TCH3  |   .2909736   .1667554     1.74   0.082    -.0371804    .6191276
TSGT, MSGT, 1SGT  |   .0710115   .1768843     0.40   0.688    -.2770749    .4190979
                  |
            _cons |   1.111042   .5398766     2.06   0.040     .0486312    2.173452
-----------------------------------------------------------------------------------

Warning: regressor matrix for use_comply equation appears ill-conditioned. (Condition number = 1860.557.)
This might prevent convergence. If it does, and if you have not done so already, you may need to remove nearly
collinear regressors to achieve convergence. Or you may need to add a nrtolerance(#) or nonrtolerance option to the command line.
See cmp tips.

      Source |       SS           df       MS      Number of obs   =       648
-------------+----------------------------------   F(36, 611)      =     17.20
       Model |  81.2612191        36  2.25725609   Prob > F        =    0.0000
    Residual |  80.1816821       611  .131230249   R-squared       =    0.5033
-------------+----------------------------------   Adj R-squared   =    0.4741
       Total |  161.442901       647   .24952535   Root MSE        =    .36226

--------------------------------------------------------------------------------------
            complier |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
                 age |
         18 or Less  |  -.0580267    .314057    -0.18   0.853    -.6747889    .5587354
                 19  |   .2517184   .2041668     1.23   0.218    -.1492354    .6526723
                 20  |   .1845914   .1807758     1.02   0.308    -.1704259    .5396087
                 21  |   .0482339   .1811139     0.27   0.790    -.3074474    .4039152
                 22  |   .2349914   .1772936     1.33   0.186    -.1131872    .5831701
                 23  |   .1290968   .1788322     0.72   0.471    -.2221035    .4802972
                 24  |   .2044587    .177114     1.15   0.249    -.1433674    .5522848
                 25  |    .109656   .1802608     0.61   0.543      -.24435     .463662
              26-29  |   .1440453   .1742154     0.83   0.409    -.1980884    .4861789
              30-34  |   .1423899   .1757027     0.81   0.418    -.2026645    .4874443
                35+  |    .158674   .1761656     0.90   0.368    -.1872896    .5046375
                     |
              school |
         <8th Grade  |  -.2679469   .2302047    -1.16   0.245    -.7200354    .1841416
          8th Grade  |  -.2449809   .2298372    -1.07   0.287    -.6963477    .2063858
   Some High School  |  -.1919939   .2283839    -0.84   0.401    -.6405066    .2565188
        High School  |  -.2075978   .2286571    -0.91   0.364     -.656647    .2414514
            College  |  -.1978111   .2311899    -0.86   0.393    -.6518344    .2562122
                     |
       monthsoversea |
   3 Months or Less  |   .1206629   .2179681     0.55   0.580    -.3073947    .5487205
         3-6 Months  |   .2178059   .2211842     0.98   0.325    -.2165676    .6521794
         6-9 Months  |    .035603   .2259656     0.16   0.875    -.4081606    .4793665
        9-12 Months  |   .2254623   .2158898     1.04   0.297    -.1985139    .6494384
       12-18 Months  |   .1125408    .215451     0.52   0.602    -.3105735    .5356551
       18-24 Months  |   .0477386   .2141347     0.22   0.824    -.3727908     .468268
       24-30 Months  |    .059309   .2134854     0.28   0.781    -.3599452    .4785632
       30-36 Months  |   .0692846   .2285666     0.30   0.762     -.379587    .5181561
         36+ Months  |   .1164172   .2174203     0.54   0.593    -.3105646     .543399
                     |
           rankgrade |
       PRV. or PFC.  |   .0528193   .1361178     0.39   0.698    -.2144962    .3201347
        CPL or TCH5  |   .0678775   .1369715     0.50   0.620    -.2011144    .3368695
        SGT or TCH4  |   .0446596   .1384256     0.32   0.747    -.2271882    .3165074
       SSGT or TCH3  |  -.1255978   .1444415    -0.87   0.385    -.4092598    .1580642
   TSGT, MSGT, 1SGT  |  -.0926349   .1557689    -0.59   0.552    -.3985424    .2132726
                     |
                base |
            Wheeler  |   .0995622   .0563472     1.77   0.078    -.0110954    .2102198
           Mokuleia  |  -.2207373   .0609901    -3.62   0.000    -.3405129   -.1009617
            Bellows  |  -.0532516   .0432227    -1.23   0.218    -.1381346    .0316315
             Kahulu  |   .1709229   .0456701     3.74   0.000     .0812334    .2606124
                     |
            training |   .7187303   .0352632    20.38   0.000     .6494786    .7879821
orientation_officers |    .029198   .0154934     1.88   0.060    -.0012288    .0596249
               _cons |   .1062798   .3383179     0.31   0.754    -.5581272    .7706868
--------------------------------------------------------------------------------------

Warning: regressor matrix for complier equation appears ill-conditioned. (Condition number = 1425.2195.)
This might prevent convergence. If it does, and if you have not done so already, you may need to remove nearly
collinear regressors to achieve convergence. Or you may need to add a nrtolerance(#) or nonrtolerance option to the command line.
See cmp tips.

Fitting full model.

Iteration 0:   log pseudolikelihood = -284.51913  
Iteration 1:   log pseudolikelihood = -282.44303  
Iteration 2:   log pseudolikelihood = -281.84664  
Iteration 3:   log pseudolikelihood = -281.84507  
Iteration 4:   log pseudolikelihood = -281.84507  

Mixed-process regression                        Number of obs     =        648
                                                Wald chi2(4)      =       5.66
Log pseudolikelihood = -281.84507               Prob > chi2       =     0.2260

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                     |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
use_comply           |
          numchamber |  -.0449363   .0136233    -3.30   0.001    -.0716375    -.018235
             nummask |   .0126136     .00885     1.43   0.154     -.004732    .0299593
                     |
                 age |
         18 or Less  |  -.1610371   .0688694    -2.34   0.019    -.2960186   -.0260556
                 19  |  -.2976897   .0870187    -3.42   0.001    -.4682432   -.1271362
                 20  |  -.1837379   .0752702    -2.44   0.015    -.3312648    -.036211
                 21  |  -.1837765    .061625    -2.98   0.003    -.3045593   -.0629936
                 22  |  -.3043243   .0870201    -3.50   0.000    -.4748807    -.133768
                 23  |   -.136726   .0812415    -1.68   0.092    -.2959563    .0225044
                 24  |  -.1717395   .0237468    -7.23   0.000    -.2182824   -.1251965
                 25  |  -.1990438   .0827596    -2.41   0.016    -.3612496    -.036838
              26-29  |  -.1513433   .0584982    -2.59   0.010    -.2659978   -.0366889
              30-34  |  -.1621082   .0281601    -5.76   0.000     -.217301   -.1069154
                35+  |  -.1935984   .0341833    -5.66   0.000    -.2605965   -.1266003
                     |
              school |
         <8th Grade  |  -.1478153   .0642997    -2.30   0.022    -.2738403   -.0217902
          8th Grade  |  -.2084003    .079057    -2.64   0.008     -.363349   -.0534515
   Some High School  |  -.2100531     .04459    -4.71   0.000    -.2974479   -.1226583
        High School  |  -.1241276   .0418222    -2.97   0.003    -.2060976   -.0421576
            College  |  -.1758351   .1076326    -1.63   0.102     -.386791    .0351209
                     |
       monthsoversea |
   3 Months or Less  |   -.062377   .1020656    -0.61   0.541    -.2624219     .137668
         3-6 Months  |  -.2324028   .0930935    -2.50   0.013    -.4148627   -.0499428
         6-9 Months  |  -.0992019    .068841    -1.44   0.150    -.2341278    .0357241
        9-12 Months  |  -.0773884   .0571577    -1.35   0.176    -.1894154    .0346386
       12-18 Months  |  -.1113835   .0896132    -1.24   0.214    -.2870221    .0642551
       18-24 Months  |  -.0927784   .0736705    -1.26   0.208      -.23717    .0516132
       24-30 Months  |  -.0856417   .0786021    -1.09   0.276     -.239699    .0684156
       30-36 Months  |   .0278612   .0841178     0.33   0.740    -.1370067    .1927291
         36+ Months  |  -.0244627   .0887796    -0.28   0.783    -.1984675    .1495421
                     |
           rankgrade |
       PRV. or PFC.  |   .2148689   .2546882     0.84   0.399    -.2843109    .7140486
        CPL or TCH5  |   .1846307   .2760366     0.67   0.504    -.3563911    .7256526
        SGT or TCH4  |   .1446694   .2695621     0.54   0.591    -.3836627    .6730014
       SSGT or TCH3  |   .3051401   .2795257     1.09   0.275    -.2427202    .8530003
   TSGT, MSGT, 1SGT  |   .0716514   .2835111     0.25   0.800      -.48402    .6273229
                     |
               _cons |   1.229931   .2529149     4.86   0.000     .7342271    1.725635
---------------------+----------------------------------------------------------------
complier             |
                 age |
         18 or Less  |  -.0551193   .1891029    -0.29   0.771    -.4257542    .3155157
                 19  |   .2548425   .1474056     1.73   0.084    -.0340671    .5437521
                 20  |   .1885949   .1298947     1.45   0.147     -.065994    .4431838
                 21  |   .0528366   .0903076     0.59   0.558    -.1241631    .2298363
                 22  |   .2388327   .0939517     2.54   0.011     .0546908    .4229747
                 23  |   .1352819   .0674563     2.01   0.045       .00307    .2674938
                 24  |   .2090031   .1098964     1.90   0.057    -.0063899     .424396
                 25  |   .1129138   .0863157     1.31   0.191    -.0562619    .2820894
              26-29  |   .1481594   .0946132     1.57   0.117     -.037279    .3335979
              30-34  |   .1463058   .0996293     1.47   0.142    -.0489641    .3415757
                35+  |   .1629821   .1143512     1.43   0.154    -.0611422    .3871064
                     |
              school |
         <8th Grade  |  -.2653772   .0615512    -4.31   0.000    -.3860154   -.1447391
          8th Grade  |     -.2426   .0838841    -2.89   0.004    -.4070099   -.0781902
   Some High School  |  -.1893528   .0614673    -3.08   0.002    -.3098265   -.0688791
        High School  |  -.2048045   .0855506    -2.39   0.017    -.3724806   -.0371285
            College  |  -.1949156   .1293093    -1.51   0.132    -.4483572     .058526
                     |
       monthsoversea |
   3 Months or Less  |   .1231995   .0839258     1.47   0.142    -.0412921    .2876911
         3-6 Months  |   .2190175   .1375572     1.59   0.111    -.0505897    .4886247
         6-9 Months  |   .0386387   .1200429     0.32   0.748     -.196641    .2739184
        9-12 Months  |   .2282214   .0878244     2.60   0.009     .0560888     .400354
       12-18 Months  |   .1143085   .1346746     0.85   0.396    -.1496488    .3782658
       18-24 Months  |    .050119   .0816311     0.61   0.539    -.1098751     .210113
       24-30 Months  |    .060122    .112854     0.53   0.594    -.1610678    .2813118
       30-36 Months  |   .0710292   .1318912     0.54   0.590    -.1874728    .3295311
         36+ Months  |    .116735   .1449707     0.81   0.421    -.1674023    .4008723
                     |
           rankgrade |
       PRV. or PFC.  |   .0565932   .1110239     0.51   0.610    -.1610097    .2741961
        CPL or TCH5  |   .0722242   .1234238     0.59   0.558     -.169682    .3141304
        SGT or TCH4  |   .0498997   .1247016     0.40   0.689    -.1945109    .2943103
       SSGT or TCH3  |  -.1205218    .104427    -1.15   0.248     -.325195    .0841513
   TSGT, MSGT, 1SGT  |   -.087327    .075263    -1.16   0.246    -.2348397    .0601857
                     |
                base |
            Wheeler  |   .0890749   .0308861     2.88   0.004     .0285393    .1496106
           Mokuleia  |  -.2396285   .0727273    -3.29   0.001    -.3821713   -.0970856
            Bellows  |  -.0550207   .0231131    -2.38   0.017    -.1003216   -.0097198
             Kahulu  |   .1608258   .0194545     8.27   0.000     .1226957    .1989559
                     |
            training |   .7213246   .0977766     7.38   0.000      .529686    .9129631
orientation_officers |    .028138   .0233382     1.21   0.228    -.0176041    .0738801
               _cons |   .0984269   .2237666     0.44   0.660    -.3401475    .5370014
---------------------+----------------------------------------------------------------
            /lnsig_1 |  -1.284977    .103451   -12.42   0.000    -1.487737   -1.082217
            /lnsig_2 |  -1.044673   .1225337    -8.53   0.000    -1.284834    -.804511
        /atanhrho_12 |    -.18068   .0823656    -2.19   0.028    -.3421137   -.0192464
---------------------+----------------------------------------------------------------
               sig_1 |   .2766569   .0286204                      .2258832    .3388434
               sig_2 |    .351807   .0431082                      .2766965    .4473066
              rho_12 |  -.1787392   .0797342                     -.3293631    -.019244
--------------------------------------------------------------------------------------
(est2 stored)

.         estadd local Demog "Yes" 

added macro:
              e(Demog) : "Yes"

. 
. eststo: cmp (use_comply= numchamber nummask i.($demographic) ally_relations) (complier = i.($demographic base) training orientation
> _officers), ind($cmp_cont $cmp_cont) cluster(base)

Fitting individual models as starting point for full model fit.
Note: For programming reasons, these initial estimates may deviate from your specification.
      For exact fits of each equation alone, run cmp separately on each.

      Source |       SS           df       MS      Number of obs   =       334
-------------+----------------------------------   F(33, 300)      =      1.07
       Model |  2.87241535        33  .087042889   Prob > F        =    0.3717
    Residual |  24.4329739       300  .081443246   R-squared       =    0.1052
-------------+----------------------------------   Adj R-squared   =    0.0068
       Total |  27.3053892       333  .081998166   Root MSE        =    .28538

-----------------------------------------------------------------------------------
       use_comply |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
       numchamber |  -.0184778   .0190782    -0.97   0.334     -.056022    .0190663
          nummask |   .0096748   .0102348     0.95   0.345    -.0104663    .0298158
                  |
              age |
      18 or Less  |  -.1217157   .4111326    -0.30   0.767    -.9307847    .6873533
              19  |  -.2817949   .3124325    -0.90   0.368    -.8966319     .333042
              20  |  -.1683064   .2969538    -0.57   0.571    -.7526826    .4160699
              21  |  -.2026121   .2977117    -0.68   0.497    -.7884799    .3832557
              22  |  -.2891442   .2943699    -0.98   0.327    -.8684356    .2901473
              23  |  -.1390968   .2970882    -0.47   0.640    -.7237375    .4455439
              24  |  -.1553855   .2954228    -0.53   0.599    -.7367488    .4259778
              25  |  -.1809764   .2995718    -0.60   0.546    -.7705046    .4085517
           26-29  |  -.1419467    .294293    -0.48   0.630    -.7210868    .4371935
           30-34  |  -.1522821   .2936312    -0.52   0.604    -.7301198    .4255556
             35+  |  -.1714283   .2951014    -0.58   0.562    -.7521591    .4093026
                  |
           school |
      <8th Grade  |  -.1236786   .3015968    -0.41   0.682    -.7171918    .4698346
       8th Grade  |  -.1670497   .2996285    -0.56   0.578    -.7566894    .4225901
Some High School  |  -.1487094   .2977266    -0.50   0.618    -.7346065    .4371877
     High School  |  -.0761741   .2985887    -0.26   0.799    -.6637678    .5114195
         College  |  -.1183885   .3018431    -0.39   0.695    -.7123865    .4756095
                  |
    monthsoversea |
3 Months or Less  |  -.1017853   .3027558    -0.34   0.737    -.6975793    .4940088
      3-6 Months  |  -.2434213   .3006588    -0.81   0.419    -.8350887     .348246
      6-9 Months  |   -.119826   .3060118    -0.39   0.696    -.7220276    .4823756
     9-12 Months  |  -.0929466   .2971534    -0.31   0.755    -.6777156    .4918224
    12-18 Months  |  -.1350267   .2980265    -0.45   0.651    -.7215139    .4514605
    18-24 Months  |   -.124708   .2965128    -0.42   0.674    -.7082163    .4588003
    24-30 Months  |  -.1206084   .2967708    -0.41   0.685    -.7046246    .4634078
    30-36 Months  |   .0086088   .3220016     0.03   0.979    -.6250592    .6422767
      36+ Months  |  -.0413099   .3011788    -0.14   0.891    -.6340005    .5513808
                  |
        rankgrade |
    PRV. or PFC.  |   .2332691   .1519527     1.54   0.126    -.0657591    .5322973
     CPL or TCH5  |   .2122168   .1517209     1.40   0.163    -.0863553    .5107889
     SGT or TCH4  |   .1533692   .1526537     1.00   0.316    -.1470385    .4537769
    SSGT or TCH3  |   .3166639   .1653467     1.92   0.056    -.0087224    .6420502
TSGT, MSGT, 1SGT  |    .058833    .175154     0.34   0.737     -.285853    .4035191
                  |
   ally_relations |   .0464702   .0173389     2.68   0.008      .012349    .0805915
            _cons |   1.120982   .5344285     2.10   0.037     .0692786    2.172685
-----------------------------------------------------------------------------------

Warning: regressor matrix for use_comply equation appears ill-conditioned. (Condition number = 1868.8363.)
This might prevent convergence. If it does, and if you have not done so already, you may need to remove nearly
collinear regressors to achieve convergence. Or you may need to add a nrtolerance(#) or nonrtolerance option to the command line.
See cmp tips.

      Source |       SS           df       MS      Number of obs   =       648
-------------+----------------------------------   F(36, 611)      =     17.20
       Model |  81.2612191        36  2.25725609   Prob > F        =    0.0000
    Residual |  80.1816821       611  .131230249   R-squared       =    0.5033
-------------+----------------------------------   Adj R-squared   =    0.4741
       Total |  161.442901       647   .24952535   Root MSE        =    .36226

--------------------------------------------------------------------------------------
            complier |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
                 age |
         18 or Less  |  -.0580267    .314057    -0.18   0.853    -.6747889    .5587354
                 19  |   .2517184   .2041668     1.23   0.218    -.1492354    .6526723
                 20  |   .1845914   .1807758     1.02   0.308    -.1704259    .5396087
                 21  |   .0482339   .1811139     0.27   0.790    -.3074474    .4039152
                 22  |   .2349914   .1772936     1.33   0.186    -.1131872    .5831701
                 23  |   .1290968   .1788322     0.72   0.471    -.2221035    .4802972
                 24  |   .2044587    .177114     1.15   0.249    -.1433674    .5522848
                 25  |    .109656   .1802608     0.61   0.543      -.24435     .463662
              26-29  |   .1440453   .1742154     0.83   0.409    -.1980884    .4861789
              30-34  |   .1423899   .1757027     0.81   0.418    -.2026645    .4874443
                35+  |    .158674   .1761656     0.90   0.368    -.1872896    .5046375
                     |
              school |
         <8th Grade  |  -.2679469   .2302047    -1.16   0.245    -.7200354    .1841416
          8th Grade  |  -.2449809   .2298372    -1.07   0.287    -.6963477    .2063858
   Some High School  |  -.1919939   .2283839    -0.84   0.401    -.6405066    .2565188
        High School  |  -.2075978   .2286571    -0.91   0.364     -.656647    .2414514
            College  |  -.1978111   .2311899    -0.86   0.393    -.6518344    .2562122
                     |
       monthsoversea |
   3 Months or Less  |   .1206629   .2179681     0.55   0.580    -.3073947    .5487205
         3-6 Months  |   .2178059   .2211842     0.98   0.325    -.2165676    .6521794
         6-9 Months  |    .035603   .2259656     0.16   0.875    -.4081606    .4793665
        9-12 Months  |   .2254623   .2158898     1.04   0.297    -.1985139    .6494384
       12-18 Months  |   .1125408    .215451     0.52   0.602    -.3105735    .5356551
       18-24 Months  |   .0477386   .2141347     0.22   0.824    -.3727908     .468268
       24-30 Months  |    .059309   .2134854     0.28   0.781    -.3599452    .4785632
       30-36 Months  |   .0692846   .2285666     0.30   0.762     -.379587    .5181561
         36+ Months  |   .1164172   .2174203     0.54   0.593    -.3105646     .543399
                     |
           rankgrade |
       PRV. or PFC.  |   .0528193   .1361178     0.39   0.698    -.2144962    .3201347
        CPL or TCH5  |   .0678775   .1369715     0.50   0.620    -.2011144    .3368695
        SGT or TCH4  |   .0446596   .1384256     0.32   0.747    -.2271882    .3165074
       SSGT or TCH3  |  -.1255978   .1444415    -0.87   0.385    -.4092598    .1580642
   TSGT, MSGT, 1SGT  |  -.0926349   .1557689    -0.59   0.552    -.3985424    .2132726
                     |
                base |
            Wheeler  |   .0995622   .0563472     1.77   0.078    -.0110954    .2102198
           Mokuleia  |  -.2207373   .0609901    -3.62   0.000    -.3405129   -.1009617
            Bellows  |  -.0532516   .0432227    -1.23   0.218    -.1381346    .0316315
             Kahulu  |   .1709229   .0456701     3.74   0.000     .0812334    .2606124
                     |
            training |   .7187303   .0352632    20.38   0.000     .6494786    .7879821
orientation_officers |    .029198   .0154934     1.88   0.060    -.0012288    .0596249
               _cons |   .1062798   .3383179     0.31   0.754    -.5581272    .7706868
--------------------------------------------------------------------------------------

Warning: regressor matrix for complier equation appears ill-conditioned. (Condition number = 1425.2195.)
This might prevent convergence. If it does, and if you have not done so already, you may need to remove nearly
collinear regressors to achieve convergence. Or you may need to add a nrtolerance(#) or nonrtolerance option to the command line.
See cmp tips.

Fitting full model.

Iteration 0:   log pseudolikelihood = -280.53522  
Iteration 1:   log pseudolikelihood = -278.42819  
Iteration 2:   log pseudolikelihood = -277.56169  
Iteration 3:   log pseudolikelihood = -277.55645  
Iteration 4:   log pseudolikelihood = -277.55645  

Mixed-process regression                        Number of obs     =        648
                                                Wald chi2(4)      =       6.12
Log pseudolikelihood = -277.55645               Prob > chi2       =     0.1904

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                     |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
use_comply           |
          numchamber |  -.0464276   .0126981    -3.66   0.000    -.0713154   -.0215397
             nummask |   .0124326   .0078984     1.57   0.115     -.003048    .0279132
                     |
                 age |
         18 or Less  |  -.1682184   .0711615    -2.36   0.018    -.3076925   -.0287444
                 19  |  -.3265746   .0678133    -4.82   0.000    -.4594863   -.1936629
                 20  |   -.214225   .0767239    -2.79   0.005    -.3646009    -.063849
                 21  |  -.2284422    .071195    -3.21   0.001    -.3679818   -.0889026
                 22  |  -.3361405   .0875572    -3.84   0.000    -.5077494   -.1645315
                 23  |  -.1713625   .0733496    -2.34   0.019    -.3151252   -.0275999
                 24  |  -.1980123   .0234218    -8.45   0.000    -.2439182   -.1521063
                 25  |  -.2191716   .0854099    -2.57   0.010    -.3865719   -.0517713
              26-29  |  -.1749344    .057059    -3.07   0.002    -.2867679   -.0631009
              30-34  |  -.1983669   .0342212    -5.80   0.000    -.2654393   -.1312945
                35+  |  -.2129808   .0333641    -6.38   0.000    -.2783733   -.1475883
                     |
              school |
         <8th Grade  |  -.1394973   .0689528    -2.02   0.043    -.2746423   -.0043523
          8th Grade  |  -.1817262   .0785182    -2.31   0.021     -.335619   -.0278333
   Some High School  |  -.1769425   .0513591    -3.45   0.001    -.2776045   -.0762806
        High School  |  -.0915052   .0492165    -1.86   0.063    -.1879678    .0049574
            College  |  -.1303904   .1141692    -1.14   0.253    -.3541579    .0933771
                     |
       monthsoversea |
   3 Months or Less  |  -.1056576    .089741    -1.18   0.239    -.2815467    .0702314
         3-6 Months  |  -.2695818    .089837    -3.00   0.003    -.4456592   -.0935045
         6-9 Months  |  -.1305424   .0717138    -1.82   0.069    -.2710989     .010014
        9-12 Months  |  -.1053854   .0485873    -2.17   0.030    -.2006146   -.0101561
       12-18 Months  |  -.1441938   .0887091    -1.63   0.104    -.3180604    .0296728
       18-24 Months  |  -.1300145   .0763448    -1.70   0.089    -.2796475    .0196185
       24-30 Months  |   -.108963   .0712785    -1.53   0.126    -.2486664    .0307403
       30-36 Months  |   .0269724   .0857485     0.31   0.753    -.1410917    .1950364
         36+ Months  |  -.0439798   .0796777    -0.55   0.581    -.2001451    .1121856
                     |
           rankgrade |
       PRV. or PFC.  |   .2348608   .2300289     1.02   0.307    -.2159876    .6857092
        CPL or TCH5  |   .2047479   .2521845     0.81   0.417    -.2895246    .6990204
        SGT or TCH4  |   .1487109   .2459024     0.60   0.545     -.333249    .6306708
       SSGT or TCH3  |   .3329534   .2541088     1.31   0.190    -.1650907    .8309976
   TSGT, MSGT, 1SGT  |   .0590913    .242604     0.24   0.808    -.4164038    .5345864
                     |
      ally_relations |   .0482024   .0176309     2.73   0.006     .0136464    .0827583
               _cons |   1.250267    .235439     5.31   0.000     .7888153    1.711719
---------------------+----------------------------------------------------------------
complier             |
                 age |
         18 or Less  |  -.0550547   .1884564    -0.29   0.770    -.4244225    .3143131
                 19  |   .2550294   .1478994     1.72   0.085    -.0348482    .5449069
                 20  |   .1890005   .1302353     1.45   0.147     -.066256    .4442569
                 21  |   .0534546   .0906956     0.59   0.556    -.1243055    .2312147
                 22  |   .2394065   .0942052     2.54   0.011     .0547678    .4240452
                 23  |   .1362402   .0679367     2.01   0.045     .0030868    .2693937
                 24  |   .2097401   .1101671     1.90   0.057    -.0061836    .4256637
                 25  |   .1135008   .0862665     1.32   0.188    -.0555784    .2825801
              26-29  |   .1487464   .0947896     1.57   0.117    -.0370378    .3345306
              30-34  |   .1469296   .0998527     1.47   0.141    -.0487781    .3426373
                35+  |   .1636573   .1147099     1.43   0.154      -.06117    .3884846
                     |
              school |
         <8th Grade  |  -.2643865    .061205    -4.32   0.000     -.384346    -.144427
          8th Grade  |   -.241799   .0840681    -2.88   0.004    -.4065695   -.0770285
   Some High School  |   -.188422   .0615559    -3.06   0.002    -.3090694   -.0677746
        High School  |   -.203923   .0856408    -2.38   0.017    -.3717759   -.0360701
            College  |  -.1940765   .1296387    -1.50   0.134    -.4481636    .0600106
                     |
       monthsoversea |
   3 Months or Less  |    .124361   .0846633     1.47   0.142     -.041576     .290298
         3-6 Months  |   .2199469   .1378591     1.60   0.111    -.0502519    .4901458
         6-9 Months  |   .0396008   .1201377     0.33   0.742    -.1958648    .2750663
        9-12 Months  |   .2295704   .0882384     2.60   0.009     .0566263    .4025145
       12-18 Months  |   .1149631   .1350331     0.85   0.395    -.1496969     .379623
       18-24 Months  |   .0508994   .0821322     0.62   0.535    -.1100767    .2118756
       24-30 Months  |   .0605791   .1131439     0.54   0.592    -.1611788    .2823371
       30-36 Months  |   .0716333   .1319024     0.54   0.587    -.1868907    .3301572
         36+ Months  |   .1171705   .1452359     0.81   0.420    -.1674867    .4018277
                     |
           rankgrade |
       PRV. or PFC.  |   .0572207   .1109745     0.52   0.606    -.1602852    .2747267
        CPL or TCH5  |   .0730204   .1232334     0.59   0.553    -.1685127    .3145535
        SGT or TCH4  |   .0508726   .1245333     0.41   0.683    -.1932083    .2949534
       SSGT or TCH3  |  -.1196115   .1039189    -1.15   0.250    -.3232888    .0840657
   TSGT, MSGT, 1SGT  |  -.0861901   .0747101    -1.15   0.249    -.2326191     .060239
                     |
                base |
            Wheeler  |   .0876755   .0314624     2.79   0.005     .0260103    .1493407
           Mokuleia  |  -.2430085   .0711917    -3.41   0.001    -.3825416   -.1034753
            Bellows  |  -.0553248    .022816    -2.42   0.015    -.1000434   -.0106062
             Kahulu  |   .1582395   .0183461     8.63   0.000     .1222818    .1941972
                     |
            training |   .7220874   .0973238     7.42   0.000     .5313362    .9128387
orientation_officers |   .0273707   .0232909     1.18   0.240    -.0182787      .07302
               _cons |   .0962732   .2245038     0.43   0.668    -.3437461    .5362925
---------------------+----------------------------------------------------------------
            /lnsig_1 |  -1.294596   .1023074   -12.65   0.000    -1.495115   -1.094077
            /lnsig_2 |  -1.044623   .1225585    -8.52   0.000    -1.284833   -.8044124
        /atanhrho_12 |  -.1983973   .0907835    -2.19   0.029    -.3763298   -.0204649
---------------------+----------------------------------------------------------------
               sig_1 |   .2740086   .0280331                      .2242229    .3348485
               sig_2 |   .3518245   .0431191                      .2766968    .4473507
              rho_12 |  -.1958346   .0873019                     -.3595158    -.020462
--------------------------------------------------------------------------------------
(est3 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

. 
. eststo: cmp (use_comply= numchamber nummask i.($demographic) ally_relations orientation_officers) (complier = i.($demographic base)
>  training orientation_officers), ind($cmp_cont $cmp_cont) cluster(base)

Fitting individual models as starting point for full model fit.
Note: For programming reasons, these initial estimates may deviate from your specification.
      For exact fits of each equation alone, run cmp separately on each.

      Source |       SS           df       MS      Number of obs   =       334
-------------+----------------------------------   F(34, 299)      =      1.03
       Model |  2.87358202        34  .084517118   Prob > F        =    0.4207
    Residual |  24.4318072       299   .08171173   R-squared       =    0.1052
-------------+----------------------------------   Adj R-squared   =    0.0035
       Total |  27.3053892       333  .081998166   Root MSE        =    .28585

--------------------------------------------------------------------------------------
          use_comply |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0188008   .0192999    -0.97   0.331    -.0567817      .01918
             nummask |   .0095864   .0102783     0.93   0.352    -.0106406    .0298133
                     |
                 age |
         18 or Less  |  -.1203968   .4119576    -0.29   0.770    -.9311003    .6903068
                 19  |  -.2805964   .3131078    -0.90   0.371    -.8967705    .3355777
                 20  |  -.1666042   .2977838    -0.56   0.576    -.7526217    .4194134
                 21  |  -.2006166   .2986693    -0.67   0.502    -.7883768    .3871436
                 22  |  -.2882365   .2949526    -0.98   0.329    -.8686824    .2922094
                 23  |  -.1372853   .2979634    -0.46   0.645    -.7236563    .4490856
                 24  |  -.1543778   .2960294    -0.52   0.602    -.7369429    .4281873
                 25  |  -.1798273   .3002192    -0.60   0.550    -.7706376    .4109829
              26-29  |  -.1405421    .295012    -0.48   0.634    -.7211049    .4400208
              30-34  |  -.1511689   .2942623    -0.51   0.608    -.7302564    .4279186
                35+  |  -.1698326   .2958889    -0.57   0.566    -.7521211    .4124558
                     |
              school |
         <8th Grade  |  -.1269938   .3033648    -0.42   0.676    -.7239944    .4700068
          8th Grade  |  -.1696053    .300883    -0.56   0.573    -.7617219    .4225114
   Some High School  |  -.1513828   .2990551    -0.51   0.613    -.7399022    .4371365
        High School  |  -.0788555   .2999211    -0.26   0.793    -.6690792    .5113682
            College  |  -.1211664   .3032327    -0.40   0.690     -.717907    .4755743
                     |
       monthsoversea |
   3 Months or Less  |  -.1000706   .3035937    -0.33   0.742    -.6975217    .4973806
         3-6 Months  |  -.2428609   .3011905    -0.81   0.421    -.8355826    .3498608
         6-9 Months  |  -.1171405   .3073387    -0.38   0.703    -.7219614    .4876804
        9-12 Months  |  -.0913578   .2979396    -0.31   0.759    -.6776819    .4949664
       12-18 Months  |  -.1331079   .2989489    -0.45   0.656    -.7214183    .4552025
       18-24 Months  |  -.1228119   .2974247    -0.41   0.680    -.7081228     .462499
       24-30 Months  |   -.118729   .2976754    -0.40   0.690    -.7045332    .4670752
       30-36 Months  |   .0112504   .3232887     0.03   0.972     -.624959    .6474597
         36+ Months  |  -.0394127   .3020923    -0.13   0.896    -.6339092    .5550837
                     |
           rankgrade |
       PRV. or PFC.  |   .2329773   .1522225     1.53   0.127    -.0665859    .5325406
        CPL or TCH5  |   .2117592   .1520191     1.39   0.165    -.0874036     .510922
        SGT or TCH4  |   .1526897   .1530108     1.00   0.319    -.1484248    .4538042
       SSGT or TCH3  |   .3163575   .1656389     1.91   0.057    -.0096082    .6423231
   TSGT, MSGT, 1SGT  |    .058486   .1754665     0.33   0.739    -.2868197    .4037917
                     |
      ally_relations |   .0463044   .0174228     2.66   0.008     .0120175    .0805913
orientation_officers |    .001872   .0156666     0.12   0.905    -.0289587    .0327027
               _cons |   1.121527   .5353281     2.10   0.037     .0680389    2.175015
--------------------------------------------------------------------------------------

Warning: regressor matrix for use_comply equation appears ill-conditioned. (Condition number = 1888.5763.)
This might prevent convergence. If it does, and if you have not done so already, you may need to remove nearly
collinear regressors to achieve convergence. Or you may need to add a nrtolerance(#) or nonrtolerance option to the command line.
See cmp tips.

      Source |       SS           df       MS      Number of obs   =       648
-------------+----------------------------------   F(36, 611)      =     17.20
       Model |  81.2612191        36  2.25725609   Prob > F        =    0.0000
    Residual |  80.1816821       611  .131230249   R-squared       =    0.5033
-------------+----------------------------------   Adj R-squared   =    0.4741
       Total |  161.442901       647   .24952535   Root MSE        =    .36226

--------------------------------------------------------------------------------------
            complier |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
                 age |
         18 or Less  |  -.0580267    .314057    -0.18   0.853    -.6747889    .5587354
                 19  |   .2517184   .2041668     1.23   0.218    -.1492354    .6526723
                 20  |   .1845914   .1807758     1.02   0.308    -.1704259    .5396087
                 21  |   .0482339   .1811139     0.27   0.790    -.3074474    .4039152
                 22  |   .2349914   .1772936     1.33   0.186    -.1131872    .5831701
                 23  |   .1290968   .1788322     0.72   0.471    -.2221035    .4802972
                 24  |   .2044587    .177114     1.15   0.249    -.1433674    .5522848
                 25  |    .109656   .1802608     0.61   0.543      -.24435     .463662
              26-29  |   .1440453   .1742154     0.83   0.409    -.1980884    .4861789
              30-34  |   .1423899   .1757027     0.81   0.418    -.2026645    .4874443
                35+  |    .158674   .1761656     0.90   0.368    -.1872896    .5046375
                     |
              school |
         <8th Grade  |  -.2679469   .2302047    -1.16   0.245    -.7200354    .1841416
          8th Grade  |  -.2449809   .2298372    -1.07   0.287    -.6963477    .2063858
   Some High School  |  -.1919939   .2283839    -0.84   0.401    -.6405066    .2565188
        High School  |  -.2075978   .2286571    -0.91   0.364     -.656647    .2414514
            College  |  -.1978111   .2311899    -0.86   0.393    -.6518344    .2562122
                     |
       monthsoversea |
   3 Months or Less  |   .1206629   .2179681     0.55   0.580    -.3073947    .5487205
         3-6 Months  |   .2178059   .2211842     0.98   0.325    -.2165676    .6521794
         6-9 Months  |    .035603   .2259656     0.16   0.875    -.4081606    .4793665
        9-12 Months  |   .2254623   .2158898     1.04   0.297    -.1985139    .6494384
       12-18 Months  |   .1125408    .215451     0.52   0.602    -.3105735    .5356551
       18-24 Months  |   .0477386   .2141347     0.22   0.824    -.3727908     .468268
       24-30 Months  |    .059309   .2134854     0.28   0.781    -.3599452    .4785632
       30-36 Months  |   .0692846   .2285666     0.30   0.762     -.379587    .5181561
         36+ Months  |   .1164172   .2174203     0.54   0.593    -.3105646     .543399
                     |
           rankgrade |
       PRV. or PFC.  |   .0528193   .1361178     0.39   0.698    -.2144962    .3201347
        CPL or TCH5  |   .0678775   .1369715     0.50   0.620    -.2011144    .3368695
        SGT or TCH4  |   .0446596   .1384256     0.32   0.747    -.2271882    .3165074
       SSGT or TCH3  |  -.1255978   .1444415    -0.87   0.385    -.4092598    .1580642
   TSGT, MSGT, 1SGT  |  -.0926349   .1557689    -0.59   0.552    -.3985424    .2132726
                     |
                base |
            Wheeler  |   .0995622   .0563472     1.77   0.078    -.0110954    .2102198
           Mokuleia  |  -.2207373   .0609901    -3.62   0.000    -.3405129   -.1009617
            Bellows  |  -.0532516   .0432227    -1.23   0.218    -.1381346    .0316315
             Kahulu  |   .1709229   .0456701     3.74   0.000     .0812334    .2606124
                     |
            training |   .7187303   .0352632    20.38   0.000     .6494786    .7879821
orientation_officers |    .029198   .0154934     1.88   0.060    -.0012288    .0596249
               _cons |   .1062798   .3383179     0.31   0.754    -.5581272    .7706868
--------------------------------------------------------------------------------------

Warning: regressor matrix for complier equation appears ill-conditioned. (Condition number = 1425.2195.)
This might prevent convergence. If it does, and if you have not done so already, you may need to remove nearly
collinear regressors to achieve convergence. Or you may need to add a nrtolerance(#) or nonrtolerance option to the command line.
See cmp tips.

Fitting full model.

Iteration 0:   log pseudolikelihood = -280.58405  
Iteration 1:   log pseudolikelihood = -278.49176  
Iteration 2:   log pseudolikelihood =  -277.5542  
Iteration 3:   log pseudolikelihood =  -277.5477  
Iteration 4:   log pseudolikelihood =  -277.5477  

Mixed-process regression                        Number of obs     =        648
                                                Wald chi2(4)      =       6.21
Log pseudolikelihood =  -277.5477               Prob > chi2       =     0.1842

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                     |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
use_comply           |
          numchamber |   -.046762    .012104    -3.86   0.000    -.0704855   -.0230386
             nummask |   .0123416   .0080293     1.54   0.124    -.0033956    .0280788
                     |
                 age |
         18 or Less  |  -.1669014   .0696162    -2.40   0.017    -.3033466   -.0304562
                 19  |  -.3253466   .0703107    -4.63   0.000     -.463153   -.1875402
                 20  |   -.212466   .0765676    -2.77   0.006    -.3625358   -.0623962
                 21  |  -.2263845    .070857    -3.19   0.001    -.3652617   -.0875072
                 22  |  -.3352147   .0863494    -3.88   0.000    -.5044564   -.1659731
                 23  |  -.1694888   .0668418    -2.54   0.011    -.3004963   -.0384814
                 24  |  -.1969829   .0260984    -7.55   0.000    -.2481347    -.145831
                 25  |  -.2179938   .0868436    -2.51   0.012    -.3882041   -.0477834
              26-29  |  -.1734893   .0529328    -3.28   0.001    -.2772357   -.0697428
              30-34  |  -.1972292   .0329833    -5.98   0.000    -.2618753    -.132583
                35+  |  -.2113429   .0352842    -5.99   0.000    -.2804986   -.1421871
                     |
              school |
         <8th Grade  |  -.1429665   .0616073    -2.32   0.020    -.2637144   -.0222185
          8th Grade  |  -.1844007   .0774664    -2.38   0.017     -.336232   -.0325694
   Some High School  |  -.1797422   .0501542    -3.58   0.000    -.2780427   -.0814417
        High School  |  -.0943078   .0459232    -2.05   0.040    -.1843157   -.0042999
            College  |  -.1332944   .1099013    -1.21   0.225     -.348697    .0821081
                     |
       monthsoversea |
   3 Months or Less  |  -.1039072    .085696    -1.21   0.225    -.2718682    .0640538
         3-6 Months  |  -.2690566   .0897481    -3.00   0.003    -.4449596   -.0931535
         6-9 Months  |  -.1277894   .0752293    -1.70   0.089    -.2752361    .0196572
        9-12 Months  |  -.1037704   .0482731    -2.15   0.032     -.198384   -.0091568
       12-18 Months  |  -.1422399   .0883126    -1.61   0.107    -.3153294    .0308496
       18-24 Months  |  -.1280844    .078541    -1.63   0.103     -.282022    .0258531
       24-30 Months  |  -.1070477   .0699633    -1.53   0.126    -.2441733    .0300779
       30-36 Months  |   .0296807   .0892981     0.33   0.740    -.1453404    .2047018
         36+ Months  |  -.0420464   .0767978    -0.55   0.584    -.1925673    .1084744
                     |
           rankgrade |
       PRV. or PFC.  |   .2345643   .2286442     1.03   0.305    -.2135702    .6826987
        CPL or TCH5  |   .2042807   .2505549     0.82   0.415    -.2867979    .6953593
        SGT or TCH4  |   .1480086   .2433707     0.61   0.543    -.3289891    .6250064
       SSGT or TCH3  |   .3326436    .253008     1.31   0.189     -.163243    .8285302
   TSGT, MSGT, 1SGT  |   .0587345   .2408993     0.24   0.807    -.4134194    .5308884
                     |
      ally_relations |   .0480318   .0181057     2.65   0.008     .0125452    .0835184
orientation_officers |   .0019695   .0068422     0.29   0.773     -.011441    .0153801
               _cons |   1.250902   .2328697     5.37   0.000     .7944861    1.707318
---------------------+----------------------------------------------------------------
complier             |
                 age |
         18 or Less  |   -.054876   .1886144    -0.29   0.771    -.4245535    .3148015
                 19  |   .2550299   .1479586     1.72   0.085    -.0349635    .5450234
                 20  |   .1889043   .1299996     1.45   0.146    -.0658903    .4436988
                 21  |   .0533864   .0906644     0.59   0.556    -.1243126    .2310854
                 22  |   .2394106   .0942632     2.54   0.011     .0546581    .4241632
                 23  |   .1361444    .067871     2.01   0.045     .0031197    .2691691
                 24  |   .2097334   .1102369     1.90   0.057    -.0063269    .4257938
                 25  |   .1134723   .0863532     1.31   0.189    -.0557769    .2827215
              26-29  |   .1487052    .094812     1.57   0.117    -.0371229    .3345333
              30-34  |   .1469166   .0999057     1.47   0.141    -.0488949    .3427281
                35+  |   .1636341   .1147525     1.43   0.154    -.0612767    .3885449
                     |
              school |
         <8th Grade  |  -.2640185   .0606871    -4.35   0.000    -.3829631   -.1450739
          8th Grade  |  -.2415029    .083508    -2.89   0.004    -.4051756   -.0778302
   Some High School  |  -.1881179      .0609    -3.09   0.002    -.3074797   -.0687562
        High School  |   -.203639   .0847928    -2.40   0.016    -.3698298   -.0374482
            College  |  -.1937779    .128761    -1.50   0.132    -.4461448     .058589
                     |
       monthsoversea |
   3 Months or Less  |   .1244137   .0849251     1.46   0.143    -.0420363    .2908638
         3-6 Months  |   .2202244    .138021     1.60   0.111    -.0502918    .4907405
         6-9 Months  |   .0396322   .1203684     0.33   0.742    -.1962855    .2755498
        9-12 Months  |   .2296567   .0884941     2.60   0.009     .0562114    .4031021
       12-18 Months  |   .1150671   .1352295     0.85   0.395    -.1499779    .3801121
       18-24 Months  |   .0510072   .0823875     0.62   0.536    -.1104693    .2124838
       24-30 Months  |   .0606887   .1134313     0.54   0.593    -.1616326    .2830099
       30-36 Months  |   .0716822   .1319161     0.54   0.587    -.1868685     .330233
         36+ Months  |   .1172529   .1455313     0.81   0.420    -.1679832    .4024891
                     |
           rankgrade |
       PRV. or PFC.  |   .0571711   .1109592     0.52   0.606    -.1603048    .2746471
        CPL or TCH5  |   .0729617   .1231787     0.59   0.554    -.1684641    .3143875
        SGT or TCH4  |   .0508638    .124569     0.41   0.683    -.1932869    .2950145
       SSGT or TCH3  |  -.1196577   .1039415    -1.15   0.250    -.3233792    .0840639
   TSGT, MSGT, 1SGT  |  -.0862208   .0747478    -1.15   0.249    -.2327238    .0602822
                     |
                base |
            Wheeler  |   .0876181   .0315394     2.78   0.005     .0258021    .1494341
           Mokuleia  |  -.2430811   .0709622    -3.43   0.001    -.3821645   -.1039977
            Bellows  |  -.0553954   .0226833    -2.44   0.015    -.0998538    -.010937
             Kahulu  |   .1582246   .0182683     8.66   0.000     .1224195    .1940297
                     |
            training |   .7220832   .0974118     7.41   0.000     .5311596    .9130069
orientation_officers |   .0270924   .0231632     1.17   0.242    -.0183066    .0724915
               _cons |    .095986    .224859     0.43   0.669    -.3447297    .5367016
---------------------+----------------------------------------------------------------
            /lnsig_1 |  -1.294616   .1021651   -12.67   0.000    -1.494856   -1.094377
            /lnsig_2 |  -1.044623   .1225591    -8.52   0.000    -1.284834   -.8044112
        /atanhrho_12 |   -.198426   .0895585    -2.22   0.027    -.3739574   -.0228947
---------------------+----------------------------------------------------------------
               sig_1 |   .2740029   .0279935                      .2242808    .3347482
               sig_2 |   .3518246   .0431193                      .2766965    .4473512
              rho_12 |  -.1958622   .0861228                     -.3574483   -.0228907
--------------------------------------------------------------------------------------
(est4 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

. 
. eststo: cmp (use_comply= numchamber nummask i.($demographic) ally_relations orientation_officers infocenter orient_meet war_interes
> t) (complier = i.($demographic base) training orientation_officers), ind($cmp_cont $cmp_cont) cluster(base)

Fitting individual models as starting point for full model fit.
Note: For programming reasons, these initial estimates may deviate from your specification.
      For exact fits of each equation alone, run cmp separately on each.

      Source |       SS           df       MS      Number of obs   =       334
-------------+----------------------------------   F(37, 296)      =      1.20
       Model |  3.55710256        37  .096137907   Prob > F        =    0.2081
    Residual |  23.7482867       296  .080230698   R-squared       =    0.1303
-------------+----------------------------------   Adj R-squared   =    0.0216
       Total |  27.3053892       333  .081998166   Root MSE        =    .28325

--------------------------------------------------------------------------------------
          use_comply |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0420991    .020769    -2.03   0.044    -.0829728   -.0012254
             nummask |   .0086068   .0101958     0.84   0.399    -.0114586    .0286722
                     |
                 age |
         18 or Less  |  -.1509782   .4121633    -0.37   0.714    -.9621199    .6601635
                 19  |   -.246694   .3126587    -0.79   0.431    -.8620097    .3686217
                 20  |  -.1324492   .2970227    -0.45   0.656    -.7169931    .4520947
                 21  |  -.1623611   .2991404    -0.54   0.588    -.7510727    .4263504
                 22  |  -.2764388   .2949868    -0.94   0.349     -.856976    .3040983
                 23  |  -.1122598   .2982906    -0.38   0.707     -.699299    .4747794
                 24  |  -.1061503   .2962302    -0.36   0.720    -.6891345    .4768339
                 25  |  -.1387343   .3004086    -0.46   0.645    -.7299417    .4524731
              26-29  |  -.1224134   .2952008    -0.41   0.679    -.7033716    .4585449
              30-34  |  -.1182994   .2942199    -0.40   0.688    -.6973273    .4607284
                35+  |  -.1191313   .2956758    -0.40   0.687    -.7010245    .4627618
                     |
              school |
         <8th Grade  |  -.0328334   .3049649    -0.11   0.914    -.6330076    .5673408
          8th Grade  |  -.0832359   .3026808    -0.27   0.784    -.6789149     .512443
   Some High School  |  -.0700618   .3007632    -0.23   0.816    -.6619669    .5218434
        High School  |   .0101828   .3021345     0.03   0.973    -.5844212    .6047868
            College  |  -.0476616   .3059039    -0.16   0.876    -.6496837    .5543605
                     |
       monthsoversea |
   3 Months or Less  |  -.0474465   .3027276    -0.16   0.876    -.6432176    .5483246
         3-6 Months  |  -.2072684   .3002785    -0.69   0.491    -.7982197     .383683
         6-9 Months  |  -.0614974   .3064901    -0.20   0.841    -.6646732    .5416784
        9-12 Months  |  -.0480766   .2967328    -0.16   0.871    -.6320499    .5358967
       12-18 Months  |  -.0748384   .2985362    -0.25   0.802    -.6623609     .512684
       18-24 Months  |   -.071026   .2969595    -0.24   0.811    -.6554455    .5133934
       24-30 Months  |  -.0838582   .2965536    -0.28   0.778    -.6674789    .4997625
       30-36 Months  |    .089886   .3222823     0.28   0.781     -.544369    .7241409
         36+ Months  |   .0045725   .3010644     0.02   0.988    -.5879256    .5970705
                     |
           rankgrade |
       PRV. or PFC.  |   .2469848   .1510784     1.63   0.103    -.0503391    .5443086
        CPL or TCH5  |   .2327367    .150956     1.54   0.124    -.0643464    .5298197
        SGT or TCH4  |   .1722015   .1520761     1.13   0.258    -.1270859    .4714889
       SSGT or TCH3  |   .3327096     .16428     2.03   0.044     .0094048    .6560144
   TSGT, MSGT, 1SGT  |   .0972131   .1749843     0.56   0.579    -.2471578    .4415841
                     |
      ally_relations |   .0486523    .017334     2.81   0.005     .0145388    .0827658
orientation_officers |  -.0134555   .0166478    -0.81   0.420    -.0462186    .0193076
          infocenter |   .0167573   .0373132     0.45   0.654    -.0566754      .09019
         orient_meet |   .1040982   .0393186     2.65   0.009     .0267189    .1814776
        war_interest |  -.0066106   .0166181    -0.40   0.691    -.0393151    .0260939
               _cons |   .9096409   .5375911     1.69   0.092    -.1483441    1.967626
--------------------------------------------------------------------------------------

Warning: regressor matrix for use_comply equation appears ill-conditioned. (Condition number = 2397.6297.)
This might prevent convergence. If it does, and if you have not done so already, you may need to remove nearly
collinear regressors to achieve convergence. Or you may need to add a nrtolerance(#) or nonrtolerance option to the command line.
See cmp tips.

      Source |       SS           df       MS      Number of obs   =       648
-------------+----------------------------------   F(36, 611)      =     17.20
       Model |  81.2612191        36  2.25725609   Prob > F        =    0.0000
    Residual |  80.1816821       611  .131230249   R-squared       =    0.5033
-------------+----------------------------------   Adj R-squared   =    0.4741
       Total |  161.442901       647   .24952535   Root MSE        =    .36226

--------------------------------------------------------------------------------------
            complier |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
                 age |
         18 or Less  |  -.0580267    .314057    -0.18   0.853    -.6747889    .5587354
                 19  |   .2517184   .2041668     1.23   0.218    -.1492354    .6526723
                 20  |   .1845914   .1807758     1.02   0.308    -.1704259    .5396087
                 21  |   .0482339   .1811139     0.27   0.790    -.3074474    .4039152
                 22  |   .2349914   .1772936     1.33   0.186    -.1131872    .5831701
                 23  |   .1290968   .1788322     0.72   0.471    -.2221035    .4802972
                 24  |   .2044587    .177114     1.15   0.249    -.1433674    .5522848
                 25  |    .109656   .1802608     0.61   0.543      -.24435     .463662
              26-29  |   .1440453   .1742154     0.83   0.409    -.1980884    .4861789
              30-34  |   .1423899   .1757027     0.81   0.418    -.2026645    .4874443
                35+  |    .158674   .1761656     0.90   0.368    -.1872896    .5046375
                     |
              school |
         <8th Grade  |  -.2679469   .2302047    -1.16   0.245    -.7200354    .1841416
          8th Grade  |  -.2449809   .2298372    -1.07   0.287    -.6963477    .2063858
   Some High School  |  -.1919939   .2283839    -0.84   0.401    -.6405066    .2565188
        High School  |  -.2075978   .2286571    -0.91   0.364     -.656647    .2414514
            College  |  -.1978111   .2311899    -0.86   0.393    -.6518344    .2562122
                     |
       monthsoversea |
   3 Months or Less  |   .1206629   .2179681     0.55   0.580    -.3073947    .5487205
         3-6 Months  |   .2178059   .2211842     0.98   0.325    -.2165676    .6521794
         6-9 Months  |    .035603   .2259656     0.16   0.875    -.4081606    .4793665
        9-12 Months  |   .2254623   .2158898     1.04   0.297    -.1985139    .6494384
       12-18 Months  |   .1125408    .215451     0.52   0.602    -.3105735    .5356551
       18-24 Months  |   .0477386   .2141347     0.22   0.824    -.3727908     .468268
       24-30 Months  |    .059309   .2134854     0.28   0.781    -.3599452    .4785632
       30-36 Months  |   .0692846   .2285666     0.30   0.762     -.379587    .5181561
         36+ Months  |   .1164172   .2174203     0.54   0.593    -.3105646     .543399
                     |
           rankgrade |
       PRV. or PFC.  |   .0528193   .1361178     0.39   0.698    -.2144962    .3201347
        CPL or TCH5  |   .0678775   .1369715     0.50   0.620    -.2011144    .3368695
        SGT or TCH4  |   .0446596   .1384256     0.32   0.747    -.2271882    .3165074
       SSGT or TCH3  |  -.1255978   .1444415    -0.87   0.385    -.4092598    .1580642
   TSGT, MSGT, 1SGT  |  -.0926349   .1557689    -0.59   0.552    -.3985424    .2132726
                     |
                base |
            Wheeler  |   .0995622   .0563472     1.77   0.078    -.0110954    .2102198
           Mokuleia  |  -.2207373   .0609901    -3.62   0.000    -.3405129   -.1009617
            Bellows  |  -.0532516   .0432227    -1.23   0.218    -.1381346    .0316315
             Kahulu  |   .1709229   .0456701     3.74   0.000     .0812334    .2606124
                     |
            training |   .7187303   .0352632    20.38   0.000     .6494786    .7879821
orientation_officers |    .029198   .0154934     1.88   0.060    -.0012288    .0596249
               _cons |   .1062798   .3383179     0.31   0.754    -.5581272    .7706868
--------------------------------------------------------------------------------------

Warning: regressor matrix for complier equation appears ill-conditioned. (Condition number = 1425.2195.)
This might prevent convergence. If it does, and if you have not done so already, you may need to remove nearly
collinear regressors to achieve convergence. Or you may need to add a nrtolerance(#) or nonrtolerance option to the command line.
See cmp tips.

Fitting full model.

Iteration 0:   log pseudolikelihood = -276.09301  
Iteration 1:   log pseudolikelihood = -274.21663  
Iteration 2:   log pseudolikelihood =  -273.0141  
Iteration 3:   log pseudolikelihood =  -273.0031  
Iteration 4:   log pseudolikelihood = -273.00309  

Mixed-process regression                        Number of obs     =        648
                                                Wald chi2(4)      =       6.41
Log pseudolikelihood = -273.00309               Prob > chi2       =     0.1707

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                     |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
use_comply           |
          numchamber |  -.0681344   .0210641    -3.23   0.001    -.1094193   -.0268495
             nummask |   .0111552   .0090079     1.24   0.216    -.0064999    .0288103
                     |
                 age |
         18 or Less  |  -.2009173   .0932383    -2.15   0.031    -.3836611   -.0181735
                 19  |  -.2989139   .0563497    -5.30   0.000    -.4093574   -.1884705
                 20  |  -.1840409   .0778499    -2.36   0.018    -.3366239    -.031458
                 21  |  -.1949066   .0695424    -2.80   0.005    -.3312072    -.058606
                 22  |  -.3278079   .0849727    -3.86   0.000    -.4943513   -.1612646
                 23  |  -.1531384   .0809286    -1.89   0.058    -.3117555    .0054788
                 24  |  -.1573121   .0098797   -15.92   0.000    -.1766759   -.1379482
                 25  |  -.1851066   .0845058    -2.19   0.028    -.3507349   -.0194782
              26-29  |  -.1629015   .0673379    -2.42   0.016    -.2948814   -.0309216
              30-34  |   -.168936   .0422356    -4.00   0.000    -.2517163   -.0861557
                35+  |  -.1677986   .0331426    -5.06   0.000    -.2327569   -.1028403
                     |
              school |
         <8th Grade  |  -.0665019    .034637    -1.92   0.055    -.1343892    .0013854
          8th Grade  |  -.1186916   .0284108    -4.18   0.000    -.1743757   -.0630074
   Some High School  |  -.1185673   .0329852    -3.59   0.000     -.183217   -.0539176
        High School  |  -.0268976   .0374241    -0.72   0.472    -.1002476    .0464523
            College  |  -.0841525   .0617409    -1.36   0.173    -.2051625    .0368575
                     |
       monthsoversea |
   3 Months or Less  |  -.0463411   .1048063    -0.44   0.658    -.2517576    .1590755
         3-6 Months  |  -.2321951   .1009793    -2.30   0.021    -.4301108   -.0342794
         6-9 Months  |  -.0662678   .0908019    -0.73   0.466    -.2442362    .1117007
        9-12 Months  |  -.0580497   .0562226    -1.03   0.302     -.168244    .0521446
       12-18 Months  |  -.0785368   .1103079    -0.71   0.476    -.2947362    .1376626
       18-24 Months  |  -.0707539    .087104    -0.81   0.417    -.2414746    .0999669
       24-30 Months  |  -.0682183   .0893189    -0.76   0.445    -.2432802    .1068436
       30-36 Months  |   .1124308    .105053     1.07   0.285    -.0934693    .3183309
         36+ Months  |   .0061685   .1063251     0.06   0.954    -.2022249    .2145619
                     |
           rankgrade |
       PRV. or PFC.  |   .2496918   .2174421     1.15   0.251    -.1764868    .6758704
        CPL or TCH5  |   .2271576   .2416518     0.94   0.347    -.2464712    .7007865
        SGT or TCH4  |    .170782    .227709     0.75   0.453    -.2755194    .6170835
       SSGT or TCH3  |   .3490996   .2456777     1.42   0.155    -.1324199     .830619
   TSGT, MSGT, 1SGT  |   .1024042   .2329063     0.44   0.660    -.3540837    .5588922
                     |
      ally_relations |   .0495918   .0191554     2.59   0.010     .0120478    .0871357
orientation_officers |  -.0129593   .0090934    -1.43   0.154     -.030782    .0048635
          infocenter |   .0316793   .0241501     1.31   0.190     -.015654    .0790126
         orient_meet |   .0933796   .0349262     2.67   0.008     .0249254    .1618338
        war_interest |  -.0083624   .0130981    -0.64   0.523    -.0340343    .0173094
               _cons |   1.054205   .1581334     6.67   0.000     .7442695    1.364141
---------------------+----------------------------------------------------------------
complier             |
                 age |
         18 or Less  |  -.0521972   .1891273    -0.28   0.783    -.4228799    .3184854
                 19  |   .2550828   .1466794     1.74   0.082    -.0324035    .5425692
                 20  |    .187659   .1298764     1.44   0.148     -.066894    .4422121
                 21  |   .0517718    .090276     0.57   0.566    -.1251659    .2287095
                 22  |   .2383914   .0943253     2.53   0.011     .0535173    .4232656
                 23  |   .1332643   .0672528     1.98   0.048     .0014513    .2650773
                 24  |   .2079296   .1104159     1.88   0.060    -.0084816    .4243408
                 25  |   .1119736   .0872687     1.28   0.199    -.0590699     .283017
              26-29  |   .1474177    .094928     1.55   0.120    -.0386377    .3334731
              30-34  |   .1457582    .100217     1.45   0.146    -.0506635    .3421799
                35+  |    .162112   .1143252     1.42   0.156    -.0619612    .3861852
                     |
              school |
         <8th Grade  |  -.2650544   .0610589    -4.34   0.000    -.3847276   -.1453812
          8th Grade  |  -.2421629   .0826293    -2.93   0.003    -.4041133   -.0802125
   Some High School  |  -.1889312   .0600677    -3.15   0.002    -.3066618   -.0712006
        High School  |  -.2043429   .0840728    -2.43   0.015    -.3691226   -.0395632
            College  |  -.1940226   .1280845    -1.51   0.130    -.4450636    .0570185
                     |
       monthsoversea |
   3 Months or Less  |   .1202787   .0847595     1.42   0.156    -.0458468    .2864041
         3-6 Months  |   .2176363   .1382922     1.57   0.116    -.0534114    .4886839
         6-9 Months  |   .0352148   .1211625     0.29   0.771    -.2022593    .2726889
        9-12 Months  |   .2247956   .0882481     2.55   0.011     .0518326    .3977586
       12-18 Months  |   .1127565   .1349597     0.84   0.403    -.1517596    .3772726
       18-24 Months  |   .0482274   .0821544     0.59   0.557    -.1127923    .2092472
       24-30 Months  |   .0587403   .1139443     0.52   0.606    -.1645865     .282067
       30-36 Months  |    .069865   .1316915     0.53   0.596    -.1882455    .3279755
         36+ Months  |   .1153521   .1460751     0.79   0.430    -.1709498    .4016541
                     |
           rankgrade |
       PRV. or PFC.  |   .0552283   .1123243     0.49   0.623    -.1649232    .2753798
        CPL or TCH5  |   .0703804   .1252316     0.56   0.574    -.1750689    .3158298
        SGT or TCH4  |   .0481433   .1266352     0.38   0.704    -.2000572    .2963438
       SSGT or TCH3  |  -.1223912   .1063223    -1.15   0.250    -.3307791    .0859966
   TSGT, MSGT, 1SGT  |  -.0898463   .0780098    -1.15   0.249    -.2427428    .0630501
                     |
                base |
            Wheeler  |   .0880263   .0304674     2.89   0.004     .0283112    .1477414
           Mokuleia  |  -.2310146   .0720616    -3.21   0.001    -.3722527   -.0897764
            Bellows  |  -.0548954   .0227391    -2.41   0.016    -.0994633   -.0103276
             Kahulu  |   .1654888   .0176807     9.36   0.000     .1308352    .2001423
                     |
            training |   .7190791     .09782     7.35   0.000     .5273554    .9108027
orientation_officers |   .0282276   .0237032     1.19   0.234    -.0182299    .0746851
               _cons |   .1014166   .2258658     0.45   0.653    -.3412722    .5441054
---------------------+----------------------------------------------------------------
            /lnsig_1 |  -1.309399   .0923724   -14.18   0.000    -1.490445   -1.128352
            /lnsig_2 |   -1.04474   .1225251    -8.53   0.000    -1.284885   -.8045952
        /atanhrho_12 |  -.1911302   .0645028    -2.96   0.003    -.3175534    -.064707
---------------------+----------------------------------------------------------------
               sig_1 |   .2699823   .0249389                      .2252723     .323566
               sig_2 |   .3517833   .0431023                      .2766825    .4472689
              rho_12 |  -.1888363   .0622027                      -.307293   -.0646168
--------------------------------------------------------------------------------------
(est5 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

. 
. eststo: cmp (use_comply= numchamber nummask i.($demographic) nummask ally_relations orientation_officers infocenter orient_meet war
> _interest honolulu_contact) (complier = i.($demographic base) training orientation_officers), ind($cmp_cont $cmp_cont) cluster(base
> )

Fitting individual models as starting point for full model fit.
Note: For programming reasons, these initial estimates may deviate from your specification.
      For exact fits of each equation alone, run cmp separately on each.
note: nummask omitted because of collinearity

      Source |       SS           df       MS      Number of obs   =       334
-------------+----------------------------------   F(38, 295)      =      1.17
       Model |  3.57240984        38  .094010785   Prob > F        =    0.2379
    Residual |  23.7329794       295  .080450778   R-squared       =    0.1308
-------------+----------------------------------   Adj R-squared   =    0.0189
       Total |  27.3053892       333  .081998166   Root MSE        =    .28364

--------------------------------------------------------------------------------------
          use_comply |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0422366   .0207999    -2.03   0.043    -.0831716   -.0013016
             nummask |   .0086943   .0102117     0.85   0.395    -.0114028    .0287914
                     |
                 age |
         18 or Less  |  -.1232494    .417595    -0.30   0.768    -.9450923    .6985934
                 19  |  -.2228285   .3178319    -0.70   0.484    -.8483337    .4026767
                 20  |  -.1081762   .3025906    -0.36   0.721     -.703686    .4873336
                 21  |  -.1389038   .3043393    -0.46   0.648    -.7378551    .4600476
                 22  |  -.2523498   .3005091    -0.84   0.402     -.843763    .3390635
                 23  |  -.0895191   .3032149    -0.30   0.768    -.6862577    .5072194
                 24  |  -.0877084    .299634    -0.29   0.770    -.6773995    .5019827
                 25  |  -.1147427   .3058072    -0.38   0.708     -.716583    .4870975
              26-29  |  -.1020791   .2992585    -0.34   0.733    -.6910313    .4868731
              30-34  |  -.0963733   .2988804    -0.32   0.747    -.6845813    .4918347
                35+  |  -.0979438   .3000389    -0.33   0.744    -.6884318    .4925442
                     |
              school |
         <8th Grade  |  -.0406918   .3059138    -0.13   0.894    -.6427419    .5613583
          8th Grade  |  -.0883279   .3033203    -0.29   0.771    -.6852739     .508618
   Some High School  |  -.0751178   .3013984    -0.25   0.803    -.6682814    .5180457
        High School  |   .0059951   .3027009     0.02   0.984    -.5897319     .601722
            College  |  -.0519315   .3064795    -0.17   0.866    -.6550949    .5512319
                     |
       monthsoversea |
   3 Months or Less  |   -.055416   .3036926    -0.18   0.855    -.6530946    .5422625
         3-6 Months  |  -.2153825   .3012649    -0.71   0.475    -.8082833    .3775183
         6-9 Months  |  -.0674739   .3072158    -0.22   0.826    -.6720863    .5371386
        9-12 Months  |  -.0562656    .297732    -0.19   0.850    -.6422135    .5296823
       12-18 Months  |  -.0827079   .2994893    -0.28   0.783    -.6721141    .5066984
       18-24 Months  |  -.0759329   .2975792    -0.26   0.799    -.6615801    .5097143
       24-30 Months  |   -.092842   .2976734    -0.31   0.755    -.6786746    .4929907
       30-36 Months  |   .0861169   .3228396     0.27   0.790    -.5492439    .7214776
         36+ Months  |  -.0077909   .3028065    -0.03   0.979    -.6037256    .5881439
                     |
           rankgrade |
       PRV. or PFC.  |   .2540928   .1521605     1.67   0.096    -.0453649    .5535504
        CPL or TCH5  |   .2399039   .1520533     1.58   0.116    -.0593428    .5391506
        SGT or TCH4  |   .1811217   .1536515     1.18   0.239    -.1212702    .4835137
       SSGT or TCH3  |   .3413616   .1656966     2.06   0.040     .0152644    .6674589
   TSGT, MSGT, 1SGT  |   .1042949   .1759746     0.59   0.554    -.2420299    .4506196
                     |
      ally_relations |   .0482914   .0173775     2.78   0.006     .0140918    .0824909
orientation_officers |  -.0139812   .0167141    -0.84   0.404    -.0468752    .0189129
          infocenter |   .0149379   .0375964     0.40   0.691    -.0590533     .088929
         orient_meet |   .1048482     .03941     2.66   0.008     .0272879    .1824086
        war_interest |  -.0075312   .0167741    -0.45   0.654    -.0405433     .025481
    honolulu_contact |   .0076693   .0175822     0.44   0.663    -.0269331    .0422717
               _cons |   .8940672   .5395106     1.66   0.099    -.1677102    1.955845
--------------------------------------------------------------------------------------

Warning: regressor matrix for use_comply equation appears ill-conditioned. (Condition number = 2411.7257.)
This might prevent convergence. If it does, and if you have not done so already, you may need to remove nearly
collinear regressors to achieve convergence. Or you may need to add a nrtolerance(#) or nonrtolerance option to the command line.
See cmp tips.

      Source |       SS           df       MS      Number of obs   =       648
-------------+----------------------------------   F(36, 611)      =     17.20
       Model |  81.2612191        36  2.25725609   Prob > F        =    0.0000
    Residual |  80.1816821       611  .131230249   R-squared       =    0.5033
-------------+----------------------------------   Adj R-squared   =    0.4741
       Total |  161.442901       647   .24952535   Root MSE        =    .36226

--------------------------------------------------------------------------------------
            complier |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
                 age |
         18 or Less  |  -.0580267    .314057    -0.18   0.853    -.6747889    .5587354
                 19  |   .2517184   .2041668     1.23   0.218    -.1492354    .6526723
                 20  |   .1845914   .1807758     1.02   0.308    -.1704259    .5396087
                 21  |   .0482339   .1811139     0.27   0.790    -.3074474    .4039152
                 22  |   .2349914   .1772936     1.33   0.186    -.1131872    .5831701
                 23  |   .1290968   .1788322     0.72   0.471    -.2221035    .4802972
                 24  |   .2044587    .177114     1.15   0.249    -.1433674    .5522848
                 25  |    .109656   .1802608     0.61   0.543      -.24435     .463662
              26-29  |   .1440453   .1742154     0.83   0.409    -.1980884    .4861789
              30-34  |   .1423899   .1757027     0.81   0.418    -.2026645    .4874443
                35+  |    .158674   .1761656     0.90   0.368    -.1872896    .5046375
                     |
              school |
         <8th Grade  |  -.2679469   .2302047    -1.16   0.245    -.7200354    .1841416
          8th Grade  |  -.2449809   .2298372    -1.07   0.287    -.6963477    .2063858
   Some High School  |  -.1919939   .2283839    -0.84   0.401    -.6405066    .2565188
        High School  |  -.2075978   .2286571    -0.91   0.364     -.656647    .2414514
            College  |  -.1978111   .2311899    -0.86   0.393    -.6518344    .2562122
                     |
       monthsoversea |
   3 Months or Less  |   .1206629   .2179681     0.55   0.580    -.3073947    .5487205
         3-6 Months  |   .2178059   .2211842     0.98   0.325    -.2165676    .6521794
         6-9 Months  |    .035603   .2259656     0.16   0.875    -.4081606    .4793665
        9-12 Months  |   .2254623   .2158898     1.04   0.297    -.1985139    .6494384
       12-18 Months  |   .1125408    .215451     0.52   0.602    -.3105735    .5356551
       18-24 Months  |   .0477386   .2141347     0.22   0.824    -.3727908     .468268
       24-30 Months  |    .059309   .2134854     0.28   0.781    -.3599452    .4785632
       30-36 Months  |   .0692846   .2285666     0.30   0.762     -.379587    .5181561
         36+ Months  |   .1164172   .2174203     0.54   0.593    -.3105646     .543399
                     |
           rankgrade |
       PRV. or PFC.  |   .0528193   .1361178     0.39   0.698    -.2144962    .3201347
        CPL or TCH5  |   .0678775   .1369715     0.50   0.620    -.2011144    .3368695
        SGT or TCH4  |   .0446596   .1384256     0.32   0.747    -.2271882    .3165074
       SSGT or TCH3  |  -.1255978   .1444415    -0.87   0.385    -.4092598    .1580642
   TSGT, MSGT, 1SGT  |  -.0926349   .1557689    -0.59   0.552    -.3985424    .2132726
                     |
                base |
            Wheeler  |   .0995622   .0563472     1.77   0.078    -.0110954    .2102198
           Mokuleia  |  -.2207373   .0609901    -3.62   0.000    -.3405129   -.1009617
            Bellows  |  -.0532516   .0432227    -1.23   0.218    -.1381346    .0316315
             Kahulu  |   .1709229   .0456701     3.74   0.000     .0812334    .2606124
                     |
            training |   .7187303   .0352632    20.38   0.000     .6494786    .7879821
orientation_officers |    .029198   .0154934     1.88   0.060    -.0012288    .0596249
               _cons |   .1062798   .3383179     0.31   0.754    -.5581272    .7706868
--------------------------------------------------------------------------------------

Warning: regressor matrix for complier equation appears ill-conditioned. (Condition number = 1425.2195.)
This might prevent convergence. If it does, and if you have not done so already, you may need to remove nearly
collinear regressors to achieve convergence. Or you may need to add a nrtolerance(#) or nonrtolerance option to the command line.
See cmp tips.

Fitting full model.

Iteration 0:   log pseudolikelihood = -276.06392  
Iteration 1:   log pseudolikelihood =  -274.2289  
Iteration 2:   log pseudolikelihood = -272.95083  
Iteration 3:   log pseudolikelihood = -272.94096  
Iteration 4:   log pseudolikelihood = -272.94096  

Mixed-process regression                        Number of obs     =        648
                                                Wald chi2(4)      =       6.45
Log pseudolikelihood = -272.94096               Prob > chi2       =     0.1682

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                     |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
use_comply           |
          numchamber |  -.0679557   .0212576    -3.20   0.001    -.1096198   -.0262916
             nummask |   .0111937   .0089344     1.25   0.210    -.0063175    .0287048
                     |
                 age |
         18 or Less  |   -.179392   .0872515    -2.06   0.040    -.3504018   -.0083822
                 19  |  -.2802922   .0620796    -4.52   0.000    -.4019661   -.1586183
                 20  |  -.1651162   .0684106    -2.41   0.016    -.2991986   -.0310338
                 21  |  -.1768045   .0607671    -2.91   0.004    -.2959058   -.0577032
                 22  |  -.3090244   .0776895    -3.98   0.000     -.461293   -.1567558
                 23  |  -.1354889   .0793817    -1.71   0.088    -.2910741    .0200963
                 24  |  -.1428027   .0093078   -15.34   0.000    -.1610456   -.1245597
                 25  |  -.1664481   .0858235    -1.94   0.052    -.3346591     .001763
              26-29  |  -.1470766   .0692837    -2.12   0.034    -.2828701   -.0112831
              30-34  |  -.1517961    .037149    -4.09   0.000    -.2246068   -.0789855
                35+  |  -.1512388   .0241627    -6.26   0.000    -.1985968   -.1038809
                     |
              school |
         <8th Grade  |  -.0720877   .0378274    -1.91   0.057     -.146228    .0020526
          8th Grade  |  -.1221703   .0292205    -4.18   0.000    -.1794415   -.0648992
   Some High School  |  -.1218773   .0337463    -3.61   0.000    -.1880187   -.0557358
        High School  |  -.0296728   .0381454    -0.78   0.437    -.1044364    .0450908
            College  |  -.0869982   .0625618    -1.39   0.164     -.209617    .0356206
                     |
       monthsoversea |
   3 Months or Less  |  -.0523756   .1031784    -0.51   0.612    -.2546016    .1498504
         3-6 Months  |  -.2380603   .0995556    -2.39   0.017    -.4331857   -.0429349
         6-9 Months  |  -.0707321    .091313    -0.77   0.439    -.2497023    .1082381
        9-12 Months  |  -.0641287   .0532073    -1.21   0.228    -.1684131    .0401558
       12-18 Months  |  -.0844446   .1098725    -0.77   0.442    -.2997908    .1309015
       18-24 Months  |  -.0744634   .0873786    -0.85   0.394    -.2457222    .0967955
       24-30 Months  |    -.07518   .0877296    -0.86   0.391    -.2471269    .0967669
       30-36 Months  |   .1093416   .1042925     1.05   0.294    -.0950679    .3137511
         36+ Months  |  -.0031992   .1035498    -0.03   0.975    -.2061531    .1997546
                     |
           rankgrade |
       PRV. or PFC.  |   .2550451   .2222935     1.15   0.251    -.1806421    .6907324
        CPL or TCH5  |   .2326469    .246249     0.94   0.345    -.2499923     .715286
        SGT or TCH4  |   .1775542   .2315544     0.77   0.443     -.276284    .6313924
       SSGT or TCH3  |   .3554773   .2518992     1.41   0.158     -.138236    .8491905
   TSGT, MSGT, 1SGT  |   .1077146   .2346629     0.46   0.646    -.3522163    .5676455
                     |
      ally_relations |   .0493077   .0188331     2.62   0.009     .0123955    .0862199
orientation_officers |  -.0133645   .0090194    -1.48   0.138    -.0310421    .0043132
          infocenter |   .0301485   .0243091     1.24   0.215    -.0174964    .0777934
         orient_meet |    .094072   .0346106     2.72   0.007     .0262366    .1619074
        war_interest |  -.0090407   .0130792    -0.69   0.489    -.0346755    .0165941
    honolulu_contact |   .0058035   .0036505     1.59   0.112    -.0013513    .0129584
               _cons |   1.040844   .1667623     6.24   0.000     .7139954    1.367692
---------------------+----------------------------------------------------------------
complier             |
                 age |
         18 or Less  |  -.0521624     .18919    -0.28   0.783    -.4229679    .3186432
                 19  |   .2550723   .1466425     1.74   0.082    -.0323416    .5424863
                 20  |   .1876232   .1298606     1.44   0.149    -.0668988    .4421452
                 21  |   .0517294   .0902232     0.57   0.566    -.1251048    .2285635
                 22  |   .2383583   .0942846     2.53   0.011     .0535638    .4231528
                 23  |   .1332001   .0672114     1.98   0.048     .0014681    .2649321
                 24  |   .2078887   .1103658     1.88   0.060    -.0084243    .4242018
                 25  |   .1119459   .0872675     1.28   0.200    -.0590953    .2829871
              26-29  |   .1473819   .0948902     1.55   0.120    -.0385995    .3333633
              30-34  |   .1457252   .1002031     1.45   0.146    -.0506693    .3421196
                35+  |   .1620749   .1142773     1.42   0.156    -.0619045    .3860544
                     |
              school |
         <8th Grade  |  -.2650918   .0610618    -4.34   0.000    -.3847708   -.1454129
          8th Grade  |  -.2422018   .0826652    -2.93   0.003    -.4042227   -.0801809
   Some High School  |  -.1889741   .0601059    -3.14   0.002    -.3067796   -.0711686
        High School  |  -.2043869   .0840919    -2.43   0.015     -.369204   -.0395697
            College  |   -.194059   .1280696    -1.52   0.130    -.4450708    .0569528
                     |
       monthsoversea |
   3 Months or Less  |   .1202082   .0847314     1.42   0.156    -.0458623    .2862787
         3-6 Months  |   .2176176   .1383011     1.57   0.116    -.0534477    .4886828
         6-9 Months  |   .0351615   .1211856     0.29   0.772     -.202358    .2726809
        9-12 Months  |    .224723   .0882505     2.55   0.011     .0517552    .3976908
       12-18 Months  |   .1127251    .134945     0.84   0.404    -.1517624    .3772125
       18-24 Months  |    .048194   .0821364     0.59   0.557    -.1127903    .2091783
       24-30 Months  |    .058717   .1139269     0.52   0.606    -.1645755    .2820096
       30-36 Months  |   .0698343   .1316969     0.53   0.596    -.1882869    .3279554
         36+ Months  |   .1153404   .1460891     0.79   0.430    -.1709889    .4016697
                     |
           rankgrade |
       PRV. or PFC.  |   .0551915   .1123073     0.49   0.623    -.1649267    .2753097
        CPL or TCH5  |   .0703271   .1252003     0.56   0.574     -.175061    .3157151
        SGT or TCH4  |   .0480841   .1265842     0.38   0.704    -.2000162    .2961845
       SSGT or TCH3  |  -.1224546   .1062542    -1.15   0.249    -.3307091    .0857999
   TSGT, MSGT, 1SGT  |  -.0899196   .0779786    -1.15   0.249    -.2427549    .0629157
                     |
                base |
            Wheeler  |     .08812   .0303681     2.90   0.004     .0285996    .1476405
           Mokuleia  |   -.230797   .0720186    -3.20   0.001    -.3719509   -.0896431
            Bellows  |  -.0548353   .0228034    -2.40   0.016    -.0995291   -.0101415
             Kahulu  |   .1656684   .0177704     9.32   0.000     .1308391    .2004977
                     |
            training |   .7190266   .0978426     7.35   0.000     .5272587    .9107946
orientation_officers |   .0282499   .0236798     1.19   0.233    -.0181618    .0746615
               _cons |   .1015088   .2258006     0.45   0.653    -.3410523    .5440698
---------------------+----------------------------------------------------------------
            /lnsig_1 |  -1.309958   .0922291   -14.20   0.000    -1.490724   -1.129192
            /lnsig_2 |  -1.044742   .1225245    -8.53   0.000    -1.284885    -.804598
        /atanhrho_12 |  -.1891273   .0641259    -2.95   0.003    -.3148117    -.063443
---------------------+----------------------------------------------------------------
               sig_1 |   .2698314   .0248863                      .2252096    .3232943
               sig_2 |   .3517827    .043102                      .2766824    .4472677
              rho_12 |  -.1869042   .0618857                     -.3048081    -.063358
--------------------------------------------------------------------------------------
(est6 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Local "Yes"

added macro:
              e(Local) : "Yes"

. 
. eststo: cmp (use_comply= numchamber nummask i.($demographic base) nummask ally_relations orientation_officers infocenter orient_mee
> t war_interest honolulu_contact) (complier = i.($demographic base) training orientation_officers), ind($cmp_cont $cmp_cont) cluster
> (base)

Fitting individual models as starting point for full model fit.
Note: For programming reasons, these initial estimates may deviate from your specification.
      For exact fits of each equation alone, run cmp separately on each.
note: nummask omitted because of collinearity

      Source |       SS           df       MS      Number of obs   =       334
-------------+----------------------------------   F(42, 291)      =      1.30
       Model |  4.30592728        42  .102522078   Prob > F        =    0.1138
    Residual |  22.9994619       291  .079035952   R-squared       =    0.1577
-------------+----------------------------------   Adj R-squared   =    0.0361
       Total |  27.3053892       333  .081998166   Root MSE        =    .28113

--------------------------------------------------------------------------------------
          use_comply |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0652327   .0239141    -2.73   0.007    -.1122992   -.0181661
             nummask |   .0116928   .0104043     1.12   0.262    -.0087845    .0321701
                     |
                 age |
         18 or Less  |  -.0715329    .418907    -0.17   0.865    -.8960047    .7529388
                 19  |  -.1938293    .315885    -0.61   0.540    -.8155382    .4278797
                 20  |  -.0854187   .3009977    -0.28   0.777    -.6778273    .5069898
                 21  |  -.0961394   .3039405    -0.32   0.752    -.6943397    .5020609
                 22  |  -.2294771   .2990467    -0.77   0.443    -.8180457    .3590916
                 23  |  -.0510197   .3023437    -0.17   0.866    -.6460773    .5440379
                 24  |  -.0437103    .299581    -0.15   0.884    -.6333305    .5459099
                 25  |  -.0931177   .3051787    -0.31   0.760     -.693755    .5075195
              26-29  |  -.0680511    .298439    -0.23   0.820    -.6554238    .5193215
              30-34  |   -.062353   .2984965    -0.21   0.835    -.6498388    .5251328
                35+  |  -.0564964   .2994319    -0.19   0.850    -.6458232    .5328303
                     |
              school |
         <8th Grade  |   .0318107   .3049872     0.10   0.917    -.5684497    .6320711
          8th Grade  |  -.0001859   .3034636    -0.00   1.000    -.5974478    .5970759
   Some High School  |   .0028628   .3014759     0.01   0.992    -.5904869    .5962124
        High School  |   .0921414    .302584     0.30   0.761    -.5033891    .6876719
            College  |   .0652483    .307664     0.21   0.832    -.5402806    .6707771
                     |
       monthsoversea |
   3 Months or Less  |  -.0423767   .3023611    -0.14   0.889    -.6374685    .5527152
         3-6 Months  |  -.1660974   .3026765    -0.55   0.584    -.7618099    .4296151
         6-9 Months  |  -.0353323   .3062975    -0.12   0.908    -.6381716     .567507
        9-12 Months  |  -.0479372   .2967912    -0.16   0.872    -.6320666    .5361922
       12-18 Months  |  -.0257986   .2990659    -0.09   0.931     -.614405    .5628078
       18-24 Months  |  -.0247545   .2974827    -0.08   0.934     -.610245     .560736
       24-30 Months  |  -.0456061    .296893    -0.15   0.878    -.6299359    .5387238
       30-36 Months  |    .186344   .3244021     0.57   0.566     -.452128    .8248159
         36+ Months  |   .0376136   .3031965     0.12   0.901    -.5591226    .6343497
                     |
           rankgrade |
       PRV. or PFC.  |   .2732679   .1525059     1.79   0.074    -.0268865    .5734223
        CPL or TCH5  |    .250911   .1521365     1.65   0.100    -.0485163    .5503384
        SGT or TCH4  |   .1983057   .1534709     1.29   0.197    -.1037481    .5003595
       SSGT or TCH3  |   .3490166   .1652757     2.11   0.036     .0237293     .674304
   TSGT, MSGT, 1SGT  |   .1001246   .1754481     0.57   0.569    -.2451836    .4454328
                     |
                base |
            Wheeler  |  -.2242953   .0850043    -2.64   0.009    -.3915964   -.0569943
           Mokuleia  |  -.1122984   .0676751    -1.66   0.098    -.2454931    .0208962
            Bellows  |   .0266669   .0582186     0.46   0.647    -.0879161    .1412498
             Kahulu  |  -.0391349   .0530846    -0.74   0.462    -.1436132    .0653435
                     |
      ally_relations |   .0506892   .0173061     2.93   0.004     .0166282    .0847503
orientation_officers |  -.0185184   .0168769    -1.10   0.273    -.0517348    .0146979
          infocenter |   .0072396   .0419518     0.17   0.863    -.0753277     .089807
         orient_meet |   .0686122   .0434458     1.58   0.115    -.0168957      .15412
        war_interest |  -.0070051   .0167576    -0.42   0.676    -.0399866    .0259764
    honolulu_contact |   .0072481   .0174537     0.42   0.678    -.0271034    .0415996
               _cons |   .8017205   .5448259     1.47   0.142    -.2705783    1.874019
--------------------------------------------------------------------------------------

Warning: regressor matrix for use_comply equation appears ill-conditioned. (Condition number = 2723.0581.)
This might prevent convergence. If it does, and if you have not done so already, you may need to remove nearly
collinear regressors to achieve convergence. Or you may need to add a nrtolerance(#) or nonrtolerance option to the command line.
See cmp tips.

      Source |       SS           df       MS      Number of obs   =       648
-------------+----------------------------------   F(36, 611)      =     17.20
       Model |  81.2612191        36  2.25725609   Prob > F        =    0.0000
    Residual |  80.1816821       611  .131230249   R-squared       =    0.5033
-------------+----------------------------------   Adj R-squared   =    0.4741
       Total |  161.442901       647   .24952535   Root MSE        =    .36226

--------------------------------------------------------------------------------------
            complier |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
                 age |
         18 or Less  |  -.0580267    .314057    -0.18   0.853    -.6747889    .5587354
                 19  |   .2517184   .2041668     1.23   0.218    -.1492354    .6526723
                 20  |   .1845914   .1807758     1.02   0.308    -.1704259    .5396087
                 21  |   .0482339   .1811139     0.27   0.790    -.3074474    .4039152
                 22  |   .2349914   .1772936     1.33   0.186    -.1131872    .5831701
                 23  |   .1290968   .1788322     0.72   0.471    -.2221035    .4802972
                 24  |   .2044587    .177114     1.15   0.249    -.1433674    .5522848
                 25  |    .109656   .1802608     0.61   0.543      -.24435     .463662
              26-29  |   .1440453   .1742154     0.83   0.409    -.1980884    .4861789
              30-34  |   .1423899   .1757027     0.81   0.418    -.2026645    .4874443
                35+  |    .158674   .1761656     0.90   0.368    -.1872896    .5046375
                     |
              school |
         <8th Grade  |  -.2679469   .2302047    -1.16   0.245    -.7200354    .1841416
          8th Grade  |  -.2449809   .2298372    -1.07   0.287    -.6963477    .2063858
   Some High School  |  -.1919939   .2283839    -0.84   0.401    -.6405066    .2565188
        High School  |  -.2075978   .2286571    -0.91   0.364     -.656647    .2414514
            College  |  -.1978111   .2311899    -0.86   0.393    -.6518344    .2562122
                     |
       monthsoversea |
   3 Months or Less  |   .1206629   .2179681     0.55   0.580    -.3073947    .5487205
         3-6 Months  |   .2178059   .2211842     0.98   0.325    -.2165676    .6521794
         6-9 Months  |    .035603   .2259656     0.16   0.875    -.4081606    .4793665
        9-12 Months  |   .2254623   .2158898     1.04   0.297    -.1985139    .6494384
       12-18 Months  |   .1125408    .215451     0.52   0.602    -.3105735    .5356551
       18-24 Months  |   .0477386   .2141347     0.22   0.824    -.3727908     .468268
       24-30 Months  |    .059309   .2134854     0.28   0.781    -.3599452    .4785632
       30-36 Months  |   .0692846   .2285666     0.30   0.762     -.379587    .5181561
         36+ Months  |   .1164172   .2174203     0.54   0.593    -.3105646     .543399
                     |
           rankgrade |
       PRV. or PFC.  |   .0528193   .1361178     0.39   0.698    -.2144962    .3201347
        CPL or TCH5  |   .0678775   .1369715     0.50   0.620    -.2011144    .3368695
        SGT or TCH4  |   .0446596   .1384256     0.32   0.747    -.2271882    .3165074
       SSGT or TCH3  |  -.1255978   .1444415    -0.87   0.385    -.4092598    .1580642
   TSGT, MSGT, 1SGT  |  -.0926349   .1557689    -0.59   0.552    -.3985424    .2132726
                     |
                base |
            Wheeler  |   .0995622   .0563472     1.77   0.078    -.0110954    .2102198
           Mokuleia  |  -.2207373   .0609901    -3.62   0.000    -.3405129   -.1009617
            Bellows  |  -.0532516   .0432227    -1.23   0.218    -.1381346    .0316315
             Kahulu  |   .1709229   .0456701     3.74   0.000     .0812334    .2606124
                     |
            training |   .7187303   .0352632    20.38   0.000     .6494786    .7879821
orientation_officers |    .029198   .0154934     1.88   0.060    -.0012288    .0596249
               _cons |   .1062798   .3383179     0.31   0.754    -.5581272    .7706868
--------------------------------------------------------------------------------------

Warning: regressor matrix for complier equation appears ill-conditioned. (Condition number = 1425.2195.)
This might prevent convergence. If it does, and if you have not done so already, you may need to remove nearly
collinear regressors to achieve convergence. Or you may need to add a nrtolerance(#) or nonrtolerance option to the command line.
See cmp tips.

Fitting full model.

Iteration 0:   log pseudolikelihood = -271.36461  
Iteration 1:   log pseudolikelihood = -268.72458  
Iteration 2:   log pseudolikelihood = -268.34037  
Iteration 3:   log pseudolikelihood = -268.33615  
Iteration 4:   log pseudolikelihood = -268.33615  

Mixed-process regression                        Number of obs     =        648
                                                Wald chi2(4)      =       6.89
Log pseudolikelihood = -268.33615               Prob > chi2       =     0.1421

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                     |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
use_comply           |
          numchamber |  -.0902554   .0391682    -2.30   0.021    -.1670237   -.0134871
             nummask |   .0148254   .0107953     1.37   0.170     -.006333    .0359837
                     |
                 age |
         18 or Less  |  -.1253131   .0861296    -1.45   0.146     -.294124    .0434977
                 19  |   -.247922   .0866227    -2.86   0.004    -.4176993   -.0781446
                 20  |   -.128609   .0579977    -2.22   0.027    -.2422825   -.0149355
                 21  |  -.1201445   .0493385    -2.44   0.015    -.2168461   -.0234429
                 22  |   -.275736    .051314    -5.37   0.000    -.3763095   -.1751625
                 23  |  -.0822187   .0701974    -1.17   0.241    -.2198031    .0553657
                 24  |  -.0871149   .0387578    -2.25   0.025    -.1630787    -.011151
                 25  |  -.1317119   .1085878    -1.21   0.225    -.3445401    .0811163
              26-29  |  -.1016503   .0670273    -1.52   0.129    -.2330214    .0297209
              30-34  |  -.1031329   .0416893    -2.47   0.013    -.1848423   -.0214234
                35+  |  -.0991789   .0244846    -4.05   0.000     -.147168   -.0511899
                     |
              school |
         <8th Grade  |   .0134552   .0558035     0.24   0.809    -.0959177    .1228282
          8th Grade  |  -.0189321   .0355532    -0.53   0.594     -.088615    .0507509
   Some High School  |  -.0238839    .054722    -0.44   0.663     -.131137    .0833692
        High School  |   .0715684   .0567744     1.26   0.207    -.0397074    .1828443
            College  |   .0427199   .0273102     1.56   0.118    -.0108072    .0962469
                     |
       monthsoversea |
   3 Months or Less  |  -.0276362   .0969132    -0.29   0.776    -.2175826    .1623103
         3-6 Months  |  -.1893487   .1083329    -1.75   0.080    -.4016773    .0229799
         6-9 Months  |  -.0315726   .1086532    -0.29   0.771     -.244529    .1813837
        9-12 Months  |  -.0480491   .0723683    -0.66   0.507    -.1898883    .0937901
       12-18 Months  |    -.03192   .1100569    -0.29   0.772    -.2476276    .1837877
       18-24 Months  |  -.0257596   .0909386    -0.28   0.777     -.203996    .1524769
       24-30 Months  |   -.033738    .083186    -0.41   0.685    -.1967797    .1293036
       30-36 Months  |   .2040946   .1228696     1.66   0.097    -.0367255    .4449146
         36+ Months  |   .0420052   .0969811     0.43   0.665    -.1480742    .2320846
                     |
           rankgrade |
       PRV. or PFC.  |   .2802051   .2206116     1.27   0.204    -.1521858    .7125959
        CPL or TCH5  |   .2542034   .2422677     1.05   0.294    -.2206325    .7290393
        SGT or TCH4  |   .2019275   .2287859     0.88   0.377    -.2464846    .6503396
       SSGT or TCH3  |   .3705417   .2475874     1.50   0.134    -.1147207    .8558041
   TSGT, MSGT, 1SGT  |   .1185089   .2293097     0.52   0.605    -.3309299    .5679476
                     |
                base |
            Wheeler  |  -.2070728   .0418363    -4.95   0.000    -.2890704   -.1250752
           Mokuleia  |  -.1098936   .0393576    -2.79   0.005    -.1870331    -.032754
            Bellows  |   .0329623   .0464719     0.71   0.478    -.0581209    .1240455
             Kahulu  |  -.0717376   .0191585    -3.74   0.000    -.1092876   -.0341877
                     |
      ally_relations |   .0525315   .0184169     2.85   0.004      .016435     .088628
orientation_officers |  -.0200408   .0094806    -2.11   0.035    -.0386224   -.0014592
          infocenter |   .0105964    .012916     0.82   0.412    -.0147184    .0359112
         orient_meet |   .0545411   .0481065     1.13   0.257    -.0397459    .1488282
        war_interest |  -.0069371   .0115458    -0.60   0.548    -.0295665    .0156923
    honolulu_contact |   .0051807   .0034547     1.50   0.134    -.0015904    .0119518
               _cons |   .9252976   .2235685     4.14   0.000     .4871113    1.363484
---------------------+----------------------------------------------------------------
complier             |
                 age |
         18 or Less  |  -.0580267   .1924649    -0.30   0.763     -.435251    .3191975
                 19  |   .2517184   .1446928     1.74   0.082    -.0318741     .535311
                 20  |   .1845914   .1285535     1.44   0.151    -.0673689    .4365517
                 21  |   .0482339   .0884615     0.55   0.586    -.1251475    .2216153
                 22  |   .2349914   .0934062     2.52   0.012     .0519187    .4180642
                 23  |   .1290968   .0651614     1.98   0.048     .0013828    .2568109
                 24  |   .2044587   .1091283     1.87   0.061    -.0094289    .4183462
                 25  |    .109656   .0877261     1.25   0.211    -.0622839    .2815959
              26-29  |   .1440453   .0937915     1.54   0.125    -.0397826    .3278732
              30-34  |   .1423899   .0993132     1.43   0.152    -.0522604    .3370403
                35+  |    .158674   .1131909     1.40   0.161    -.0631761    .3805241
                     |
              school |
         <8th Grade  |  -.2679469   .0612491    -4.37   0.000     -.387993   -.1479009
          8th Grade  |  -.2449809    .081528    -3.00   0.003    -.4047728   -.0851891
   Some High School  |  -.1919939   .0585242    -3.28   0.001    -.3066992   -.0772885
        High School  |  -.2075978   .0825107    -2.52   0.012    -.3693158   -.0458797
            College  |  -.1978111   .1258175    -1.57   0.116    -.4444088    .0487866
                     |
       monthsoversea |
   3 Months or Less  |   .1206629   .0853793     1.41   0.158    -.0466775    .2880032
         3-6 Months  |   .2178059   .1395396     1.56   0.119    -.0556867    .4912984
         6-9 Months  |    .035603   .1229133     0.29   0.772    -.2053026    .2765085
        9-12 Months  |   .2254623   .0904892     2.49   0.013     .0481068    .4028178
       12-18 Months  |   .1125408   .1350901     0.83   0.405     -.152231    .3773126
       18-24 Months  |   .0477386   .0821852     0.58   0.561    -.1133414    .2088186
       24-30 Months  |    .059309   .1138352     0.52   0.602     -.163804     .282422
       30-36 Months  |   .0692846   .1320624     0.52   0.600     -.189553    .3281221
         36+ Months  |   .1164172   .1470496     0.79   0.429    -.1717947    .4046291
                     |
           rankgrade |
       PRV. or PFC.  |   .0528193   .1121746     0.47   0.638     -.167039    .2726775
        CPL or TCH5  |   .0678775   .1248542     0.54   0.587    -.1768323    .3125873
        SGT or TCH4  |   .0446596   .1261113     0.35   0.723    -.2025139    .2918331
       SSGT or TCH3  |  -.1255978   .1062376    -1.18   0.237    -.3338196    .0826241
   TSGT, MSGT, 1SGT  |  -.0926349   .0785321    -1.18   0.238     -.246555    .0612852
                     |
                base |
            Wheeler  |   .0995622   .0286471     3.48   0.001     .0434149    .1557096
           Mokuleia  |  -.2207373   .0733164    -3.01   0.003    -.3644347   -.0770398
            Bellows  |  -.0532516   .0221943    -2.40   0.016    -.0967516   -.0097515
             Kahulu  |   .1709229   .0194417     8.79   0.000     .1328178    .2090279
                     |
            training |   .7187304   .0977952     7.35   0.000     .5270553    .9104054
orientation_officers |    .029198   .0235428     1.24   0.215    -.0169451    .0753411
               _cons |   .1062798   .2244568     0.47   0.636    -.3336474     .546207
---------------------+----------------------------------------------------------------
            /lnsig_1 |  -1.325123    .089265   -14.84   0.000    -1.500079   -1.150167
            /lnsig_2 |  -1.044798    .122505    -8.53   0.000    -1.284903   -.8046924
        /atanhrho_12 |  -.1809984   .0536748    -3.37   0.001    -.2861991   -.0757977
---------------------+----------------------------------------------------------------
               sig_1 |   .2657702    .023724                      .2231124    .3165839
               sig_2 |   .3517629   .0430927                      .2766774    .4472255
              rho_12 |  -.1790475   .0519541                     -.2786327   -.0756529
--------------------------------------------------------------------------------------
(est7 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Local "Yes"

added macro:
              e(Local) : "Yes"

.         estadd local Base "Yes"

added macro:
               e(Base) : "Yes"

.         
. esttab using "${result}/CW_CMP.tex", style(tex) b(3) se(3) nonotes keep(numchamber nummask) stats(N aic cov Demog ForRel Officer In
> fo Local Base, fmt(0 0 3 3) labels("Observations" "AIC" "\hline &\\ \textsc{Parameters}" "\hspace{3mm}Demographics" "\hspace{3mm}Po
> stwar Foreign Policy" "\hspace{3mm}Officers' Leadership" "\hspace{3mm}Information Access" "\hspace{3mm}Local Contact" "\hspace{3mm}
> Airbase FE")) varlabels(numchamber "& \\ Gas Exposure" nummask "& \\ Gas Mask Training") starlevels(* .10 ** .05 *** .01) nolines p
> rehead(\begin{tabular}{l*{9}{c}} \hline & \\ & & \multicolumn{7}{c}{\textbf{Support for Using Chemical Weapons Against Japan (=1)}}
>  \\ \cline{3-9} & \\) posthead(\hline) prefoot() postfoot(& \\ \hline \end{tabular}) extracols(1) nomtit replace
(output written to ~/Desktop/JOP Replication/Results/CW_CMP.tex)

. 
. eststo clear

. 
. ********************************************************************************
. *                                                                       PEARL HARBOR                                               
>         *
. ********************************************************************************
. 
. gen pearlharbor=(monthsoversea>=8)

. 
. gen numchamberx=numchamber*pearlharbor

. gen nummaskx=nummask*pearlharbor

. 
. eststo clear

. 
. eststo: reghdfe use numchamber nummask ally_relations orientation_officers infocenter orient_meet war_interest honolulu_contact, cl
> uster(base) abs($demographic base)
(MWFE estimator converged in 9 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        634
Absorbing 5 HDFE groups                           F(   8,      4) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.0840
                                                  Adj R-squared   =     0.0172
                                                  Within R-sq.    =     0.0367
Number of clusters (base)    =          5         Root MSE        =     0.2882

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |   -.026248   .0068786    -3.82   0.019     -.045346     -.00715
             nummask |   .0024635   .0064859     0.38   0.723    -.0155442    .0204713
      ally_relations |   .0430913   .0080686     5.34   0.006     .0206894    .0654932
orientation_officers |  -.0228486   .0167005    -1.37   0.243    -.0692167    .0235195
          infocenter |   .0110018   .0325469     0.34   0.752    -.0793629    .1013665
         orient_meet |   .0616907   .0346553     1.78   0.150    -.0345279    .1579093
        war_interest |   .0033358   .0166075     0.20   0.851     -.042774    .0494456
    honolulu_contact |   .0103066    .016226     0.64   0.560     -.034744    .0553572
               _cons |   .8938628   .0179752    49.73   0.000     .8439557    .9437698
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
          base |         5           5           0    *|
-------------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation
(est1 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Local "Yes"

added macro:
              e(Local) : "Yes"

.         estadd local Base "Yes"

added macro:
               e(Base) : "Yes"

.         estadd local Cluster "Yes"

added macro:
            e(Cluster) : "Yes"

. 
. eststo: reghdfe use numchamber numchamberx nummask nummaskx, cluster(base) abs($demographic)
(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =        634
Absorbing 4 HDFE groups                           F(   4,      4) =      34.90
Statistics robust to heteroskedasticity           Prob > F        =     0.0023
                                                  R-squared       =     0.0534
                                                  Adj R-squared   =    -0.0003
                                                  Within R-sq.    =     0.0084
Number of clusters (base)    =          5         Root MSE        =     0.2908

                                   (Std. Err. adjusted for 5 clusters in base)
------------------------------------------------------------------------------
             |               Robust
         use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  numchamber |  -.0247339   .0068861    -3.59   0.023    -.0438529    -.005615
 numchamberx |    .049119   .0245582     2.00   0.116    -.0190655    .1173036
     nummask |   .0013261   .0082044     0.16   0.879    -.0214531    .0241053
    nummaskx |   .0248483   .0167211     1.49   0.211    -.0215768    .0712735
       _cons |    .921561   .0200589    45.94   0.000     .8658687    .9772533
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est2 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local Cluster "Yes"

added macro:
            e(Cluster) : "Yes"

. 
. eststo: reghdfe use numchamber numchamberx nummask nummaskx ally_relations, cluster(base) abs($demographic)
(MWFE estimator converged in 8 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        634
Absorbing 4 HDFE groups                           F(   5,      4) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.0729
                                                  Adj R-squared   =     0.0187
                                                  Within R-sq.    =     0.0289
Number of clusters (base)    =          5         Root MSE        =     0.2880

                                     (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------
               |               Robust
           use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
    numchamber |  -.0241695   .0057644    -4.19   0.014    -.0401741    -.008165
   numchamberx |   .0541316   .0250919     2.16   0.097    -.0155348     .123798
       nummask |   .0010816    .008069     0.13   0.900    -.0213216    .0234849
      nummaskx |   .0274945   .0148732     1.85   0.138       -.0138     .068789
ally_relations |   .0428909   .0065801     6.52   0.003     .0246216    .0611602
         _cons |   .9204271   .0218262    42.17   0.000     .8598278    .9810265
--------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est3 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Cluster "Yes"

added macro:
            e(Cluster) : "Yes"

. 
. eststo: reghdfe use numchamber numchamberx nummask nummaskx ally_relations orientation_officers, cluster(base) abs($demographic)
(MWFE estimator converged in 8 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        634
Absorbing 4 HDFE groups                           F(   6,      4) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.0745
                                                  Adj R-squared   =     0.0186
                                                  Within R-sq.    =     0.0305
Number of clusters (base)    =          5         Root MSE        =     0.2880

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0212893   .0071844    -2.96   0.041    -.0412365   -.0013421
         numchamberx |   .0527596   .0252535     2.09   0.105    -.0173554    .1228745
             nummask |   .0019617   .0075871     0.26   0.809    -.0191034    .0230269
            nummaskx |   .0290934   .0146928     1.98   0.119    -.0117003     .069887
      ally_relations |   .0441211    .006482     6.81   0.002     .0261243    .0621179
orientation_officers |  -.0121873   .0105486    -1.16   0.312    -.0414749    .0171004
               _cons |   .9152452    .021217    43.14   0.000     .8563373    .9741532
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est4 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Cluster "Yes"

added macro:
            e(Cluster) : "Yes"

. 
. eststo: reghdfe use numchamber numchamberx nummask nummaskx ally_relations orientation_officers infocenter orient_meet war_interest
> , cluster(base) abs($demographic)
(MWFE estimator converged in 8 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        634
Absorbing 4 HDFE groups                           F(   9,      4) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.0862
                                                  Adj R-squared   =     0.0262
                                                  Within R-sq.    =     0.0428
Number of clusters (base)    =          5         Root MSE        =     0.2869

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0347324   .0075617    -4.59   0.010    -.0557271   -.0137376
         numchamberx |   .0583452   .0218866     2.67   0.056    -.0024217    .1191121
             nummask |   .0006114   .0064988     0.09   0.930    -.0174321     .018655
            nummaskx |   .0281175   .0140258     2.00   0.115    -.0108243    .0670592
      ally_relations |   .0445923   .0070761     6.30   0.003     .0249458    .0642388
orientation_officers |  -.0204507   .0146763    -1.39   0.236    -.0611986    .0202973
          infocenter |   .0145585   .0298264     0.49   0.651    -.0682528    .0973698
         orient_meet |   .0671547   .0285471     2.35   0.078    -.0121048    .1464142
        war_interest |   .0045429   .0159606     0.28   0.790    -.0397708    .0488566
               _cons |   .8887574   .0208559    42.61   0.000     .8308522    .9466627
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est5 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Cluster "Yes"

added macro:
            e(Cluster) : "Yes"

. 
. eststo: reghdfe use numchamber numchamberx nummask nummaskx ally_relations orientation_officers infocenter orient_meet war_interest
>  honolulu_contact, cluster(base) abs($demographic)
(MWFE estimator converged in 8 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        634
Absorbing 4 HDFE groups                           F(  10,      4) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.0871
                                                  Adj R-squared   =     0.0255
                                                  Within R-sq.    =     0.0437
Number of clusters (base)    =          5         Root MSE        =     0.2870

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0341786   .0073419    -4.66   0.010    -.0545629   -.0137944
         numchamberx |   .0562331   .0198112     2.84   0.047     .0012284    .1112377
             nummask |   .0010162   .0062042     0.16   0.878    -.0162092    .0182417
            nummaskx |   .0278447   .0132769     2.10   0.104    -.0090178    .0647073
      ally_relations |   .0438466   .0076664     5.72   0.005     .0225613    .0651318
orientation_officers |  -.0211954   .0150309    -1.41   0.231     -.062928    .0205372
          infocenter |    .013484   .0293807     0.46   0.670    -.0680898    .0950578
         orient_meet |   .0676484   .0282102     2.40   0.075    -.0106756    .1459725
        war_interest |   .0040593   .0162662     0.25   0.815    -.0411031    .0492216
    honolulu_contact |   .0090664   .0162398     0.56   0.606    -.0360225    .0541553
               _cons |   .8880158   .0204728    43.38   0.000     .8311742    .9448575
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est6 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Local "Yes"

added macro:
              e(Local) : "Yes"

.         estadd local Cluster "Yes"

added macro:
            e(Cluster) : "Yes"

. 
. eststo: reghdfe use numchamber numchamberx nummask nummaskx ally_relations orientation_officers infocenter orient_meet war_interest
>  honolulu_contact, cluster(base) abs($demographic base)
(MWFE estimator converged in 9 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        634
Absorbing 5 HDFE groups                           F(  10,      4) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.0895
                                                  Adj R-squared   =     0.0198
                                                  Within R-sq.    =     0.0426
Number of clusters (base)    =          5         Root MSE        =     0.2878

                                           (Std. Err. adjusted for 5 clusters in base)
--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0348521   .0060847    -5.73   0.005    -.0517459   -.0179583
         numchamberx |    .057484   .0187181     3.07   0.037     .0055144    .1094537
             nummask |  -.0003874   .0069707    -0.06   0.958    -.0197412    .0189665
            nummaskx |   .0275509   .0140168     1.97   0.121     -.011366    .0664678
      ally_relations |   .0445119   .0079933     5.57   0.005     .0223188     .066705
orientation_officers |  -.0234309   .0166749    -1.41   0.233    -.0697278     .022866
          infocenter |   .0079672   .0328343     0.24   0.820    -.0831953    .0991297
         orient_meet |   .0610776   .0340015     1.80   0.147    -.0333258    .1554809
        war_interest |   .0033909   .0161675     0.21   0.844    -.0414973     .048279
    honolulu_contact |   .0087878   .0159791     0.55   0.612    -.0355772    .0531528
               _cons |   .8982732   .0197674    45.44   0.000     .8433901    .9531563
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
          base |         5           5           0    *|
-------------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation
(est7 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Local "Yes"

added macro:
              e(Local) : "Yes"

.         estadd local Base "Yes"

added macro:
               e(Base) : "Yes"

.         estadd local Cluster "Yes"

added macro:
            e(Cluster) : "Yes"

. 
. eststo: reghdfe use numchamber numchamberx nummask nummaskx ally_relations orientation_officers infocenter orient_meet war_interest
>  honolulu_contact, vce(robust) abs($demographic base)
(MWFE estimator converged in 9 iterations)

HDFE Linear regression                            Number of obs   =        634
Absorbing 5 HDFE groups                           F(  10,    589) =       3.17
                                                  Prob > F        =     0.0006
                                                  R-squared       =     0.0895
                                                  Adj R-squared   =     0.0215
                                                  Within R-sq.    =     0.0426
                                                  Root MSE        =     0.2876

--------------------------------------------------------------------------------------
                     |               Robust
                 use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
          numchamber |  -.0348521    .016081    -2.17   0.031    -.0664351   -.0032691
         numchamberx |    .057484    .033362     1.72   0.085    -.0080388    .1230069
             nummask |  -.0003874   .0074707    -0.05   0.959    -.0150598     .014285
            nummaskx |   .0275509    .018727     1.47   0.142    -.0092289    .0643307
      ally_relations |   .0445119   .0096876     4.59   0.000     .0254853    .0635384
orientation_officers |  -.0234309   .0129854    -1.80   0.072    -.0489343    .0020725
          infocenter |   .0079672   .0281862     0.28   0.778    -.0473906    .0633249
         orient_meet |   .0610776   .0301343     2.03   0.043     .0018939    .1202612
        war_interest |   .0033909   .0122225     0.28   0.782    -.0206141    .0273958
    honolulu_contact |   .0087878   .0118335     0.74   0.458    -.0144533    .0320289
               _cons |   .8982732   .0285803    31.43   0.000     .8421415     .954405
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
           age |        12           0          12     |
        school |         6           1           5     |
 monthsoversea |        10           1           9    ?|
     rankgrade |         6           1           5    ?|
          base |         5           1           4    ?|
-------------------------------------------------------+
? = number of redundant parameters may be higher
(est8 stored)

.         estadd local Demog "Yes"

added macro:
              e(Demog) : "Yes"

.         estadd local ForRel "Yes"

added macro:
             e(ForRel) : "Yes"

.         estadd local Officer "Yes"

added macro:
            e(Officer) : "Yes"

.         estadd local Info "Yes"

added macro:
               e(Info) : "Yes"

.         estadd local Local "Yes"

added macro:
              e(Local) : "Yes"

.         estadd local Base "Yes"

added macro:
               e(Base) : "Yes"

.         estadd local Cluster "No"

added macro:
            e(Cluster) : "No"

.         
. esttab using "${result}/CW_Combat.tex", style(tex) b(3) se(3) nonotes keep(numchamber numchamberx nummask nummaskx) stats(N aic cov
>  Cluster Demog ForRel Officer Info Local Base, fmt(0 0 3 3) labels("Observations" "AIC" "\hline &\\ \textsc{Parameters}" "\hspace{3
> mm}Airbase Clustered SEs" "\hspace{3mm}Demographics" "\hspace{3mm}Postwar Foreign Policy" "\hspace{3mm}Officers' Leadership" "\hspa
> ce{3mm}Information Access" "\hspace{3mm}Local Contact" "\hspace{3mm}Airbase FE")) varlabels(numchamber "& \\ Gas Exposure" numchamb
> erx "& \\ Gas Exposure x Pearl Harbor" nummask "& \\ Gas Mask Training" nummaskx "& \\ Gas Mask Training x Pearl Harbor") starlevel
> s(* .10 ** .05 *** .01) nolines prehead(\begin{tabular}{l*{11}{c}} \hline & \\ & & \multicolumn{9}{c}{\textbf{Support for Using Che
> mical Weapons Against Japan (=1)}} \\ \cline{3-11} & \\ & & \textbf{Baseline} & & \multicolumn{7}{c}{\textbf{Additional Controls}}\
> \ \cline{2-3} \cline{5-11} & \\) posthead(\hline) prefoot() postfoot(& \\ \hline \end{tabular}) extracols(1 2) nomtit replace
(output written to ~/Desktop/JOP Replication/Results/CW_Combat.tex)

. 
. eststo clear

. 
. ********************************************************************************
. 
. clear

. 
end of do-file

. 
. if c(username) == "christopherblair"{
. global dir "~/Desktop/JOP Replication"
. global raw "${dir}/Raw Files"
. global code "${dir}/Code"                                                                               
. }

. cd "${dir}"
/Users/christopherblair/Desktop/JOP Replication

. 
. do "${code}/CW Heterogeneous.do"

. ********************************************************************************
. *                                                CHEMICAL WEAPONS ANALYSIS                                                         
> *
. ********************************************************************************
. 
. clear all

. set more off

. set scheme plotplainblind

. macro drop _all

. est drop _all

. set matsize 800

. set seed 8675309

. 
. ** Set Working Directory
. 
. if c(username) == "christopherblair"{
. global dir "~/Desktop/JOP Replication"
. global raw "${dir}/Raw Files"
. global code "${dir}/Code"
. global result "${dir}/Results"                                                                          
. }

. 
. else if c(username) == "youruser"{
. global dir "~/Desktop/JOP Replication"
. global raw "${dir}/Raw Files"
. global code "${dir}/Code"
. global result "${dir}/Results"  
. }

. 
. cd "$raw"
/Users/christopherblair/Desktop/JOP Replication/Raw Files

. 
. ********************************************************************************
. 
. use "${dir}/ams175.dta", clear

. 
. sort ballotnum

. 
. global demographic "rankgrade age school monthsoversea"

. global covariates "ally_relations orientation_officers infocenter orient_meet war_interest honolulu_contact"

. 
. ********************************************************************************
. *                                                       HETEROGENEOUS EFFECTS                                                      
> *
. ********************************************************************************
. 
. eststo clear

. 
. reg use c.numchamber##c.lowedu c.nummask##c.lowedu ally_relations orientation_officers infocenter orient_meet war_interest honolulu
> _contact 0.school i.(age rankgrade monthsoversea base), cluster(base)
note: lowedu omitted because of collinearity

Linear regression                               Number of obs     =        634
                                                F(3, 4)           =          .
                                                Prob > F          =          .
                                                R-squared         =     0.0765
                                                Root MSE          =     .28892

                                            (Std. Err. adjusted for 5 clusters in base)
---------------------------------------------------------------------------------------
                      |               Robust
                  use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
           numchamber |  -.0149729    .009064    -1.65   0.174    -.0401386    .0101929
               lowedu |   .0037782   .0139724     0.27   0.800    -.0350153    .0425718
                      |
c.numchamber#c.lowedu |  -.0064414   .0022354    -2.88   0.045     -.012648   -.0002348
                      |
              nummask |   .0016736   .0164274     0.10   0.924    -.0439362    .0472834
               lowedu |          0  (omitted)
                      |
   c.nummask#c.lowedu |   .0012334   .0090961     0.14   0.899    -.0240214    .0264882
                      |
       ally_relations |   .0432869   .0100138     4.32   0.012     .0154841    .0710898
 orientation_officers |  -.0229105    .017463    -1.31   0.260    -.0713955    .0255745
           infocenter |   .0131655   .0339131     0.39   0.718    -.0809924    .1073234
          orient_meet |   .0599914   .0376826     1.59   0.187    -.0446324    .1646152
         war_interest |    .003082   .0177839     0.17   0.871    -.0462939    .0524579
     honolulu_contact |   .0101695   .0152442     0.67   0.541    -.0321553    .0524943
                      |
               school |
           No Answer  |  -.2660073   .0728344    -3.65   0.022    -.4682282   -.0637865
                      |
                  age |
          18 or Less  |   .0743399   .0961567     0.77   0.483    -.1926338    .3413136
                  19  |  -.1310519   .2062237    -0.64   0.560    -.7036207    .4415168
                  20  |     .03834   .1056841     0.36   0.735    -.2550862    .3317662
                  21  |   .0181812   .0925631     0.20   0.854     -.238815    .2751774
                  22  |  -.0018619   .1131746    -0.02   0.988    -.3160849     .312361
                  23  |  -.0178875     .10893    -0.16   0.878    -.3203255    .2845506
                  24  |   .0658286    .124061     0.53   0.624    -.2786201    .4102773
                  25  |  -.0245202    .121821    -0.20   0.850    -.3627494     .313709
               26-29  |   .0572214   .1025552     0.56   0.607    -.2275174    .3419602
               30-34  |   .0579077   .0880814     0.66   0.547    -.1866454    .3024607
                 35+  |   .0058659   .0896805     0.07   0.951    -.2431272    .2548589
                      |
            rankgrade |
        PRV. or PFC.  |   .0948329      .1499     0.63   0.561    -.3213561    .5110219
         CPL or TCH5  |   .1098867   .1783144     0.62   0.571    -.3851934    .6049667
         SGT or TCH4  |   .0671556   .1453471     0.46   0.668    -.3363927    .4707038
        SSGT or TCH3  |   .1145355   .1316788     0.87   0.433    -.2510635    .4801345
    TSGT, MSGT, 1SGT  |   .0223888   .1553422     0.14   0.892    -.4089103    .4536878
                      |
        monthsoversea |
    3 Months or Less  |  -.0439795   .0188412    -2.33   0.080     -.096291    .0083321
          3-6 Months  |  -.1119387   .0496512    -2.25   0.087    -.2497926    .0259152
          6-9 Months  |   .0054636   .0319944     0.17   0.873     -.083367    .0942941
         9-12 Months  |  -.0122314   .0360343    -0.34   0.751    -.1122786    .0878158
        12-18 Months  |  -.0849117   .0424636    -2.00   0.116    -.2028094     .032986
        18-24 Months  |  -.0083025   .0342923    -0.24   0.821    -.1035132    .0869081
        24-30 Months  |  -.0688609   .0368772    -1.87   0.135    -.1712486    .0335267
        30-36 Months  |  -.0449292   .0677122    -0.66   0.543    -.2329283    .1430699
          36+ Months  |  -.0230804   .0571135    -0.40   0.707    -.1816529    .1354922
                      |
                 base |
             Wheeler  |   -.016723   .0207652    -0.81   0.466    -.0743765    .0409306
            Mokuleia  |  -.0525252   .0356843    -1.47   0.215    -.1516008    .0465503
             Bellows  |  -.0293279   .0145094    -2.02   0.113    -.0696126    .0109568
              Kahulu  |   -.014242    .013947    -1.02   0.365    -.0529651    .0244812
                      |
                _cons |   .8272924   .1851117     4.47   0.011     .3133398    1.341245
---------------------------------------------------------------------------------------

. eststo: margins, dydx(numchamber) at(lowedu=(0(1)4)) atmeans vsquish post

Conditional marginal effects                    Number of obs     =        634
Model VCE    : Robust

Expression   : Linear prediction, predict()
dy/dx w.r.t. : numchamber
1._at        : numchamber      =    1.184543 (mean)
               lowedu          =           0
               nummask         =    2.055205 (mean)
               ally_relat~s    =   -.0064078 (mean)
               orientatio~s    =    .0139995 (mean)
               infocenter      =    .5567823 (mean)
               orient_meet     =    .5457413 (mean)
               war_interest    =   -.0050959 (mean)
               honolulu_c~t    =   -.0071435 (mean)
               0.school        =    .0047319 (mean)
               0.age           =    .0078864 (mean)
               1.age           =    .0031546 (mean)
               2.age           =    .0189274 (mean)
               3.age           =    .0694006 (mean)
               4.age           =    .0694006 (mean)
               5.age           =    .1009464 (mean)
               6.age           =    .0804416 (mean)
               7.age           =    .1041009 (mean)
               8.age           =    .0630915 (mean)
               9.age           =    .1876972 (mean)
               10.age          =    .1845426 (mean)
               11.age          =    .1104101 (mean)
               0.rankgrade     =     .011041 (mean)
               1.rankgrade     =    .3974763 (mean)
               2.rankgrade     =    .2839117 (mean)
               3.rankgrade     =    .1876972 (mean)
               4.rankgrade     =    .0820189 (mean)
               5.rankgrade     =    .0378549 (mean)
               0.monthsov~a    =    .0047319 (mean)
               1.monthsov~a    =    .0851735 (mean)
               2.monthsov~a    =     .055205 (mean)
               3.monthsov~a    =    .0347003 (mean)
               4.monthsov~a    =      .20347 (mean)
               5.monthsov~a    =    .1167192 (mean)
               6.monthsov~a    =    .1577287 (mean)
               7.monthsov~a    =    .2223975 (mean)
               8.monthsov~a    =    .0268139 (mean)
               9.monthsov~a    =    .0930599 (mean)
               1.base          =    .2507886 (mean)
               2.base          =    .1072555 (mean)
               3.base          =    .1041009 (mean)
               4.base          =    .2634069 (mean)
               5.base          =    .2744479 (mean)
2._at        : numchamber      =    1.184543 (mean)
               lowedu          =           1
               nummask         =    2.055205 (mean)
               ally_relat~s    =   -.0064078 (mean)
               orientatio~s    =    .0139995 (mean)
               infocenter      =    .5567823 (mean)
               orient_meet     =    .5457413 (mean)
               war_interest    =   -.0050959 (mean)
               honolulu_c~t    =   -.0071435 (mean)
               0.school        =    .0047319 (mean)
               0.age           =    .0078864 (mean)
               1.age           =    .0031546 (mean)
               2.age           =    .0189274 (mean)
               3.age           =    .0694006 (mean)
               4.age           =    .0694006 (mean)
               5.age           =    .1009464 (mean)
               6.age           =    .0804416 (mean)
               7.age           =    .1041009 (mean)
               8.age           =    .0630915 (mean)
               9.age           =    .1876972 (mean)
               10.age          =    .1845426 (mean)
               11.age          =    .1104101 (mean)
               0.rankgrade     =     .011041 (mean)
               1.rankgrade     =    .3974763 (mean)
               2.rankgrade     =    .2839117 (mean)
               3.rankgrade     =    .1876972 (mean)
               4.rankgrade     =    .0820189 (mean)
               5.rankgrade     =    .0378549 (mean)
               0.monthsov~a    =    .0047319 (mean)
               1.monthsov~a    =    .0851735 (mean)
               2.monthsov~a    =     .055205 (mean)
               3.monthsov~a    =    .0347003 (mean)
               4.monthsov~a    =      .20347 (mean)
               5.monthsov~a    =    .1167192 (mean)
               6.monthsov~a    =    .1577287 (mean)
               7.monthsov~a    =    .2223975 (mean)
               8.monthsov~a    =    .0268139 (mean)
               9.monthsov~a    =    .0930599 (mean)
               1.base          =    .2507886 (mean)
               2.base          =    .1072555 (mean)
               3.base          =    .1041009 (mean)
               4.base          =    .2634069 (mean)
               5.base          =    .2744479 (mean)
3._at        : numchamber      =    1.184543 (mean)
               lowedu          =           2
               nummask         =    2.055205 (mean)
               ally_relat~s    =   -.0064078 (mean)
               orientatio~s    =    .0139995 (mean)
               infocenter      =    .5567823 (mean)
               orient_meet     =    .5457413 (mean)
               war_interest    =   -.0050959 (mean)
               honolulu_c~t    =   -.0071435 (mean)
               0.school        =    .0047319 (mean)
               0.age           =    .0078864 (mean)
               1.age           =    .0031546 (mean)
               2.age           =    .0189274 (mean)
               3.age           =    .0694006 (mean)
               4.age           =    .0694006 (mean)
               5.age           =    .1009464 (mean)
               6.age           =    .0804416 (mean)
               7.age           =    .1041009 (mean)
               8.age           =    .0630915 (mean)
               9.age           =    .1876972 (mean)
               10.age          =    .1845426 (mean)
               11.age          =    .1104101 (mean)
               0.rankgrade     =     .011041 (mean)
               1.rankgrade     =    .3974763 (mean)
               2.rankgrade     =    .2839117 (mean)
               3.rankgrade     =    .1876972 (mean)
               4.rankgrade     =    .0820189 (mean)
               5.rankgrade     =    .0378549 (mean)
               0.monthsov~a    =    .0047319 (mean)
               1.monthsov~a    =    .0851735 (mean)
               2.monthsov~a    =     .055205 (mean)
               3.monthsov~a    =    .0347003 (mean)
               4.monthsov~a    =      .20347 (mean)
               5.monthsov~a    =    .1167192 (mean)
               6.monthsov~a    =    .1577287 (mean)
               7.monthsov~a    =    .2223975 (mean)
               8.monthsov~a    =    .0268139 (mean)
               9.monthsov~a    =    .0930599 (mean)
               1.base          =    .2507886 (mean)
               2.base          =    .1072555 (mean)
               3.base          =    .1041009 (mean)
               4.base          =    .2634069 (mean)
               5.base          =    .2744479 (mean)
4._at        : numchamber      =    1.184543 (mean)
               lowedu          =           3
               nummask         =    2.055205 (mean)
               ally_relat~s    =   -.0064078 (mean)
               orientatio~s    =    .0139995 (mean)
               infocenter      =    .5567823 (mean)
               orient_meet     =    .5457413 (mean)
               war_interest    =   -.0050959 (mean)
               honolulu_c~t    =   -.0071435 (mean)
               0.school        =    .0047319 (mean)
               0.age           =    .0078864 (mean)
               1.age           =    .0031546 (mean)
               2.age           =    .0189274 (mean)
               3.age           =    .0694006 (mean)
               4.age           =    .0694006 (mean)
               5.age           =    .1009464 (mean)
               6.age           =    .0804416 (mean)
               7.age           =    .1041009 (mean)
               8.age           =    .0630915 (mean)
               9.age           =    .1876972 (mean)
               10.age          =    .1845426 (mean)
               11.age          =    .1104101 (mean)
               0.rankgrade     =     .011041 (mean)
               1.rankgrade     =    .3974763 (mean)
               2.rankgrade     =    .2839117 (mean)
               3.rankgrade     =    .1876972 (mean)
               4.rankgrade     =    .0820189 (mean)
               5.rankgrade     =    .0378549 (mean)
               0.monthsov~a    =    .0047319 (mean)
               1.monthsov~a    =    .0851735 (mean)
               2.monthsov~a    =     .055205 (mean)
               3.monthsov~a    =    .0347003 (mean)
               4.monthsov~a    =      .20347 (mean)
               5.monthsov~a    =    .1167192 (mean)
               6.monthsov~a    =    .1577287 (mean)
               7.monthsov~a    =    .2223975 (mean)
               8.monthsov~a    =    .0268139 (mean)
               9.monthsov~a    =    .0930599 (mean)
               1.base          =    .2507886 (mean)
               2.base          =    .1072555 (mean)
               3.base          =    .1041009 (mean)
               4.base          =    .2634069 (mean)
               5.base          =    .2744479 (mean)
5._at        : numchamber      =    1.184543 (mean)
               lowedu          =           4
               nummask         =    2.055205 (mean)
               ally_relat~s    =   -.0064078 (mean)
               orientatio~s    =    .0139995 (mean)
               infocenter      =    .5567823 (mean)
               orient_meet     =    .5457413 (mean)
               war_interest    =   -.0050959 (mean)
               honolulu_c~t    =   -.0071435 (mean)
               0.school        =    .0047319 (mean)
               0.age           =    .0078864 (mean)
               1.age           =    .0031546 (mean)
               2.age           =    .0189274 (mean)
               3.age           =    .0694006 (mean)
               4.age           =    .0694006 (mean)
               5.age           =    .1009464 (mean)
               6.age           =    .0804416 (mean)
               7.age           =    .1041009 (mean)
               8.age           =    .0630915 (mean)
               9.age           =    .1876972 (mean)
               10.age          =    .1845426 (mean)
               11.age          =    .1104101 (mean)
               0.rankgrade     =     .011041 (mean)
               1.rankgrade     =    .3974763 (mean)
               2.rankgrade     =    .2839117 (mean)
               3.rankgrade     =    .1876972 (mean)
               4.rankgrade     =    .0820189 (mean)
               5.rankgrade     =    .0378549 (mean)
               0.monthsov~a    =    .0047319 (mean)
               1.monthsov~a    =    .0851735 (mean)
               2.monthsov~a    =     .055205 (mean)
               3.monthsov~a    =    .0347003 (mean)
               4.monthsov~a    =      .20347 (mean)
               5.monthsov~a    =    .1167192 (mean)
               6.monthsov~a    =    .1577287 (mean)
               7.monthsov~a    =    .2223975 (mean)
               8.monthsov~a    =    .0268139 (mean)
               9.monthsov~a    =    .0930599 (mean)
               1.base          =    .2507886 (mean)
               2.base          =    .1072555 (mean)
               3.base          =    .1041009 (mean)
               4.base          =    .2634069 (mean)
               5.base          =    .2744479 (mean)

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
numchamber   |
         _at |
          1  |  -.0149729    .009064    -1.65   0.174    -.0401386    .0101929
          2  |  -.0214142    .008824    -2.43   0.072    -.0459135     .003085
          3  |  -.0278556   .0091412    -3.05   0.038    -.0532358   -.0024755
          4  |   -.034297   .0099628    -3.44   0.026    -.0619581   -.0066359
          5  |  -.0407384   .0111779    -3.64   0.022    -.0717733   -.0097035
------------------------------------------------------------------------------
(est1 stored)

. coefplot (est1, msym(O) mfcolor(white) mlcolor(black) msize(large) ciopts(lwidth(.6 1.15) lcolor(black))), vert ci(95 90) ylabel(-.
> 1(.02).06) ymtick(-.1(.01).06) legend(off) ytitle("AME of Gas Chamber Exposure on Support" "for Using Chemical Weapons Against Japa
> n") title(" ") xlabel(1 "College" 2 "High School" 3 "Some High School" 4 "8th Grade" 5 "<8th Grade", angle(45)) yline(0, lcolor(cra
> nberry) lpatt(shortdash)) yline(-.1, lcolor(gs10) lpatt(dot))  yline(.06, lcolor(gs10) lpatt(dot)) title("Educational Attainment") 
> saving("${result}/chamber_edu.gph", replace)
(file ~/Desktop/JOP Replication/Results/chamber_edu.gph saved)

. graph export "${result}/chamber_edu.png", replace
(file ~/Desktop/JOP Replication/Results/chamber_edu.png written in PNG format)

. 
. eststo clear

. 
. gen reverse=information_center*-1

. 
. reg use c.numchamber##c.reverse c.nummask##c.reverse ally_relations orientation_officers infocenter orient_meet war_interest honolu
> lu_contact i.(age rankgrade monthsoversea base), cluster(base)
note: reverse omitted because of collinearity

Linear regression                               Number of obs     =        634
                                                F(3, 4)           =          .
                                                Prob > F          =          .
                                                R-squared         =     0.0746
                                                Root MSE          =     .28897

                                             (Std. Err. adjusted for 5 clusters in base)
----------------------------------------------------------------------------------------
                       |               Robust
                   use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
            numchamber |  -.0293111   .0063622    -4.61   0.010    -.0469754   -.0116467
               reverse |   .0087643   .0185691     0.47   0.662    -.0427918    .0603203
                       |
c.numchamber#c.reverse |  -.0127574   .0113936    -1.12   0.326    -.0443911    .0188763
                       |
               nummask |   .0036279   .0059356     0.61   0.574    -.0128518    .0201077
               reverse |          0  (omitted)
                       |
   c.nummask#c.reverse |  -.0015032   .0062407    -0.24   0.822    -.0188301    .0158238
                       |
        ally_relations |   .0413682   .0097226     4.25   0.013      .014374    .0683623
  orientation_officers |  -.0226579   .0152287    -1.49   0.211    -.0649395    .0196236
            infocenter |   .0019667   .0282656     0.07   0.948    -.0765113    .0804447
           orient_meet |   .0626009   .0379445     1.65   0.174      -.04275    .1679517
          war_interest |   .0005028   .0190313     0.03   0.980    -.0523366    .0533422
      honolulu_contact |   .0107073   .0149887     0.71   0.514    -.0309079    .0523226
                       |
                   age |
           18 or Less  |   .1382444    .118613     1.17   0.309    -.1910781    .4675669
                   19  |  -.0800217   .2294353    -0.35   0.745    -.7170362    .5569928
                   20  |   .0881807   .1364891     0.65   0.553    -.2907739    .4671353
                   21  |   .0692188   .1225653     0.56   0.602    -.2710771    .4095146
                   22  |   .0485509    .143535     0.34   0.752    -.3499662    .4470681
                   23  |   .0313053   .1376598     0.23   0.831    -.3508997    .4135103
                   24  |   .1181829   .1464699     0.81   0.465    -.2884827    .5248485
                   25  |   .0223483   .1450908     0.15   0.885    -.3804883    .4251849
                26-29  |   .1071948    .131891     0.81   0.462    -.2589932    .4733828
                30-34  |    .109672   .1164499     0.94   0.400    -.2136449    .4329888
                  35+  |   .0544363    .106836     0.51   0.637     -.242188    .3510606
                       |
             rankgrade |
         PRV. or PFC.  |   .1251934   .1309402     0.96   0.393     -.238355    .4887417
          CPL or TCH5  |   .1417257   .1582027     0.90   0.421    -.2975154    .5809669
          SGT or TCH4  |   .0974997   .1244164     0.78   0.477    -.2479356    .4429349
         SSGT or TCH3  |   .1424604   .1128384     1.26   0.275    -.1708291    .4557499
     TSGT, MSGT, 1SGT  |   .0569058   .1246136     0.46   0.672    -.2890771    .4028887
                       |
         monthsoversea |
     3 Months or Less  |  -.0289669   .0297988    -0.97   0.386    -.1117017    .0537678
           3-6 Months  |  -.1015063   .0393428    -2.58   0.061    -.2107393    .0077267
           6-9 Months  |   .0184018   .0318468     0.58   0.594     -.070019    .1068227
          9-12 Months  |   -.000789    .041107    -0.02   0.986    -.1149204    .1133424
         12-18 Months  |  -.0696113    .042993    -1.62   0.181    -.1889789    .0497563
         18-24 Months  |   .0030399   .0267647     0.11   0.915    -.0712707    .0773506
         24-30 Months  |  -.0548851   .0357233    -1.54   0.199    -.1540689    .0442988
         30-36 Months  |  -.0450122   .0755434    -0.60   0.583    -.2547543    .1647299
           36+ Months  |  -.0069113   .0572184    -0.12   0.910     -.165775    .1519525
                       |
                  base |
              Wheeler  |  -.0136722   .0200239    -0.68   0.532    -.0692675    .0419232
             Mokuleia  |  -.0455319   .0338317    -1.35   0.250    -.1394636    .0483999
              Bellows  |  -.0276436   .0145832    -1.90   0.131     -.068133    .0128459
               Kahulu  |   -.007794   .0199304    -0.39   0.716    -.0631297    .0475418
                       |
                 _cons |   .7422352   .2078691     3.57   0.023      .165098    1.319372
----------------------------------------------------------------------------------------

. eststo: margins, dydx(numchamber) at(reverse=(-1.348781 .2618844  1.086703)) atmeans vsquish post

Conditional marginal effects                    Number of obs     =        634
Model VCE    : Robust

Expression   : Linear prediction, predict()
dy/dx w.r.t. : numchamber
1._at        : numchamber      =    1.184543 (mean)
               reverse         =   -1.348781
               nummask         =    2.055205 (mean)
               ally_relat~s    =   -.0064078 (mean)
               orientatio~s    =    .0139995 (mean)
               infocenter      =    .5567823 (mean)
               orient_meet     =    .5457413 (mean)
               war_interest    =   -.0050959 (mean)
               honolulu_c~t    =   -.0071435 (mean)
               0.age           =    .0078864 (mean)
               1.age           =    .0031546 (mean)
               2.age           =    .0189274 (mean)
               3.age           =    .0694006 (mean)
               4.age           =    .0694006 (mean)
               5.age           =    .1009464 (mean)
               6.age           =    .0804416 (mean)
               7.age           =    .1041009 (mean)
               8.age           =    .0630915 (mean)
               9.age           =    .1876972 (mean)
               10.age          =    .1845426 (mean)
               11.age          =    .1104101 (mean)
               0.rankgrade     =     .011041 (mean)
               1.rankgrade     =    .3974763 (mean)
               2.rankgrade     =    .2839117 (mean)
               3.rankgrade     =    .1876972 (mean)
               4.rankgrade     =    .0820189 (mean)
               5.rankgrade     =    .0378549 (mean)
               0.monthsov~a    =    .0047319 (mean)
               1.monthsov~a    =    .0851735 (mean)
               2.monthsov~a    =     .055205 (mean)
               3.monthsov~a    =    .0347003 (mean)
               4.monthsov~a    =      .20347 (mean)
               5.monthsov~a    =    .1167192 (mean)
               6.monthsov~a    =    .1577287 (mean)
               7.monthsov~a    =    .2223975 (mean)
               8.monthsov~a    =    .0268139 (mean)
               9.monthsov~a    =    .0930599 (mean)
               1.base          =    .2507886 (mean)
               2.base          =    .1072555 (mean)
               3.base          =    .1041009 (mean)
               4.base          =    .2634069 (mean)
               5.base          =    .2744479 (mean)
2._at        : numchamber      =    1.184543 (mean)
               reverse         =    .2618844
               nummask         =    2.055205 (mean)
               ally_relat~s    =   -.0064078 (mean)
               orientatio~s    =    .0139995 (mean)
               infocenter      =    .5567823 (mean)
               orient_meet     =    .5457413 (mean)
               war_interest    =   -.0050959 (mean)
               honolulu_c~t    =   -.0071435 (mean)
               0.age           =    .0078864 (mean)
               1.age           =    .0031546 (mean)
               2.age           =    .0189274 (mean)
               3.age           =    .0694006 (mean)
               4.age           =    .0694006 (mean)
               5.age           =    .1009464 (mean)
               6.age           =    .0804416 (mean)
               7.age           =    .1041009 (mean)
               8.age           =    .0630915 (mean)
               9.age           =    .1876972 (mean)
               10.age          =    .1845426 (mean)
               11.age          =    .1104101 (mean)
               0.rankgrade     =     .011041 (mean)
               1.rankgrade     =    .3974763 (mean)
               2.rankgrade     =    .2839117 (mean)
               3.rankgrade     =    .1876972 (mean)
               4.rankgrade     =    .0820189 (mean)
               5.rankgrade     =    .0378549 (mean)
               0.monthsov~a    =    .0047319 (mean)
               1.monthsov~a    =    .0851735 (mean)
               2.monthsov~a    =     .055205 (mean)
               3.monthsov~a    =    .0347003 (mean)
               4.monthsov~a    =      .20347 (mean)
               5.monthsov~a    =    .1167192 (mean)
               6.monthsov~a    =    .1577287 (mean)
               7.monthsov~a    =    .2223975 (mean)
               8.monthsov~a    =    .0268139 (mean)
               9.monthsov~a    =    .0930599 (mean)
               1.base          =    .2507886 (mean)
               2.base          =    .1072555 (mean)
               3.base          =    .1041009 (mean)
               4.base          =    .2634069 (mean)
               5.base          =    .2744479 (mean)
3._at        : numchamber      =    1.184543 (mean)
               reverse         =    1.086703
               nummask         =    2.055205 (mean)
               ally_relat~s    =   -.0064078 (mean)
               orientatio~s    =    .0139995 (mean)
               infocenter      =    .5567823 (mean)
               orient_meet     =    .5457413 (mean)
               war_interest    =   -.0050959 (mean)
               honolulu_c~t    =   -.0071435 (mean)
               0.age           =    .0078864 (mean)
               1.age           =    .0031546 (mean)
               2.age           =    .0189274 (mean)
               3.age           =    .0694006 (mean)
               4.age           =    .0694006 (mean)
               5.age           =    .1009464 (mean)
               6.age           =    .0804416 (mean)
               7.age           =    .1041009 (mean)
               8.age           =    .0630915 (mean)
               9.age           =    .1876972 (mean)
               10.age          =    .1845426 (mean)
               11.age          =    .1104101 (mean)
               0.rankgrade     =     .011041 (mean)
               1.rankgrade     =    .3974763 (mean)
               2.rankgrade     =    .2839117 (mean)
               3.rankgrade     =    .1876972 (mean)
               4.rankgrade     =    .0820189 (mean)
               5.rankgrade     =    .0378549 (mean)
               0.monthsov~a    =    .0047319 (mean)
               1.monthsov~a    =    .0851735 (mean)
               2.monthsov~a    =     .055205 (mean)
               3.monthsov~a    =    .0347003 (mean)
               4.monthsov~a    =      .20347 (mean)
               5.monthsov~a    =    .1167192 (mean)
               6.monthsov~a    =    .1577287 (mean)
               7.monthsov~a    =    .2223975 (mean)
               8.monthsov~a    =    .0268139 (mean)
               9.monthsov~a    =    .0930599 (mean)
               1.base          =    .2507886 (mean)
               2.base          =    .1072555 (mean)
               3.base          =    .1041009 (mean)
               4.base          =    .2634069 (mean)
               5.base          =    .2744479 (mean)

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
numchamber   |
         _at |
          1  |  -.0121041   .0157348    -0.77   0.485     -.055791    .0315827
          2  |   -.032652   .0074177    -4.40   0.012    -.0532468   -.0120573
          3  |  -.0431746   .0147372    -2.93   0.043    -.0840917   -.0022574
------------------------------------------------------------------------------
(est1 stored)

. coefplot (est1, msym(O) mfcolor(white) mlcolor(black) msize(large) ciopts(lwidth(.6 1.15) lcolor(black))), vert ci(95 90) ylabel(-.
> 1(.02).06) ymtick(-.1(.01).06) legend(off) ytitle("AME of Gas Chamber Exposure on Support" "for Using Chemical Weapons Against Japa
> n") title(" ") xlabel(1 "25th Percentile" 2 "50th Percentile" 3 "75th Percentile", angle(45)) yline(0, lcolor(cranberry) lpatt(shor
> tdash)) yline(-.1, lcolor(gs10) lpatt(dot)) yline(.06, lcolor(gs10) lpatt(dot)) title("Low Information Access") saving("${result}/c
> hamber_info.gph", replace)
(file ~/Desktop/JOP Replication/Results/chamber_info.gph saved)

. graph export "${result}/chamber_info.png", replace
(file ~/Desktop/JOP Replication/Results/chamber_info.png written in PNG format)

. 
. eststo clear

. 
. replace reverse=general_interest*-1
(648 real changes made)

. 
. reg use c.numchamber##c.reverse c.nummask##c.reverse ally_relations orientation_officers infocenter orient_meet war_interest honolu
> lu_contact i.(age rankgrade monthsoversea base), cluster(base)
note: reverse omitted because of collinearity

Linear regression                               Number of obs     =        634
                                                F(3, 4)           =          .
                                                Prob > F          =          .
                                                R-squared         =     0.0747
                                                Root MSE          =     .28895

                                             (Std. Err. adjusted for 5 clusters in base)
----------------------------------------------------------------------------------------
                       |               Robust
                   use |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
            numchamber |  -.0269828   .0079411    -3.40   0.027    -.0490308   -.0049348
               reverse |   .0408002   .0349246     1.17   0.308     -.056166    .1377664
                       |
c.numchamber#c.reverse |   -.004526   .0206389    -0.22   0.837    -.0618287    .0527766
                       |
               nummask |   .0034072   .0055548     0.61   0.573    -.0120156    .0188299
               reverse |          0  (omitted)
                       |
   c.nummask#c.reverse |  -.0035082   .0038482    -0.91   0.414    -.0141927    .0071762
                       |
        ally_relations |    .041394   .0086384     4.79   0.009     .0174101     .065378
  orientation_officers |  -.0220643   .0175915    -1.25   0.278    -.0709063    .0267777
            infocenter |    .017664   .0328702     0.54   0.620    -.0735982    .1089262
           orient_meet |   .0608027   .0349134     1.74   0.157    -.0361323    .1577378
          war_interest |   .0293583   .0239135     1.23   0.287    -.0370362    .0957528
      honolulu_contact |   .0113408    .015554     0.73   0.506     -.031844    .0545256
                       |
                   age |
           18 or Less  |    .132878   .1040157     1.28   0.271    -.1559159     .421672
                   19  |  -.0754734   .2200247    -0.34   0.749    -.6863599    .5354131
                   20  |   .0912748     .12034     0.76   0.490    -.2428425    .4253921
                   21  |   .0741572    .102802     0.72   0.511    -.2112669    .3595813
                   22  |   .0520868   .1245519     0.42   0.697    -.2937248    .3978985
                   23  |   .0377666   .1188455     0.32   0.767    -.2922014    .3677347
                   24  |   .1218277   .1267741     0.96   0.391    -.2301535    .4738089
                   25  |   .0317722   .1235678     0.26   0.810    -.3113071    .3748514
                26-29  |   .1112648   .1094747     1.02   0.367    -.1926858    .4152154
                30-34  |   .1120773   .0971005     1.15   0.313     -.157517    .3816715
                  35+  |   .0566534   .0933922     0.61   0.577    -.2026449    .3159518
                       |
             rankgrade |
         PRV. or PFC.  |   .1296602   .1240933     1.04   0.355    -.2148781    .4741986
          CPL or TCH5  |   .1477264   .1523666     0.97   0.387     -.275311    .5707639
          SGT or TCH4  |   .1032557   .1147247     0.90   0.419    -.2152711    .4217825
         SSGT or TCH3  |   .1521765   .1065667     1.43   0.226    -.1437001    .4480531
     TSGT, MSGT, 1SGT  |   .0592972   .1262876     0.47   0.663    -.2913335    .4099279
                       |
         monthsoversea |
     3 Months or Less  |  -.0358031    .012859    -2.78   0.050    -.0715054   -.0001008
           3-6 Months  |  -.1142609   .0629709    -1.81   0.144    -.2890962    .0605744
           6-9 Months  |    .014258    .044506     0.32   0.765    -.1093104    .1378265
          9-12 Months  |  -.0080504   .0427984    -0.19   0.860    -.1268778     .110777
         12-18 Months  |  -.0831993   .0592656    -1.40   0.233    -.2477468    .0813483
         18-24 Months  |  -.0086011   .0428479    -0.20   0.851     -.127566    .1103639
         24-30 Months  |  -.0678748   .0482119    -1.41   0.232    -.2017325     .065983
         30-36 Months  |   -.063544   .0737327    -0.86   0.437    -.2682588    .1411708
           36+ Months  |  -.0221238    .067722    -0.33   0.760    -.2101502    .1659026
                       |
                  base |
              Wheeler  |  -.0116843   .0173354    -0.67   0.537    -.0598151    .0364464
             Mokuleia  |  -.0410827   .0280461    -1.46   0.217    -.1189513    .0367859
              Bellows  |  -.0216296   .0123391    -1.75   0.154    -.0558884    .0126293
               Kahulu  |   -.010695   .0107249    -1.00   0.375     -.040472     .019082
                       |
                 _cons |   .7349142   .2057507     3.57   0.023     .1636586     1.30617
----------------------------------------------------------------------------------------

. eststo: margins, dydx(numchamber) at(reverse=(-.8912646 -.3420756 .5774525)) atmeans vsquish post

Conditional marginal effects                    Number of obs     =        634
Model VCE    : Robust

Expression   : Linear prediction, predict()
dy/dx w.r.t. : numchamber
1._at        : numchamber      =    1.184543 (mean)
               reverse         =   -.8912646
               nummask         =    2.055205 (mean)
               ally_relat~s    =   -.0064078 (mean)
               orientatio~s    =    .0139995 (mean)
               infocenter      =    .5567823 (mean)
               orient_meet     =    .5457413 (mean)
               war_interest    =   -.0050959 (mean)
               honolulu_c~t    =   -.0071435 (mean)
               0.age           =    .0078864 (mean)
               1.age           =    .0031546 (mean)
               2.age           =    .0189274 (mean)
               3.age           =    .0694006 (mean)
               4.age           =    .0694006 (mean)
               5.age           =    .1009464 (mean)
               6.age           =    .0804416 (mean)
               7.age           =    .1041009 (mean)
               8.age           =    .0630915 (mean)
               9.age           =    .1876972 (mean)
               10.age          =    .1845426 (mean)
               11.age          =    .1104101 (mean)
               0.rankgrade     =     .011041 (mean)
               1.rankgrade     =    .3974763 (mean)
               2.rankgrade     =    .2839117 (mean)
               3.rankgrade     =    .1876972 (mean)
               4.rankgrade     =    .0820189 (mean)
               5.rankgrade     =    .0378549 (mean)
               0.monthsov~a    =    .0047319 (mean)
               1.monthsov~a    =    .0851735 (mean)
               2.monthsov~a    =     .055205 (mean)
               3.monthsov~a    =    .0347003 (mean)
               4.monthsov~a    =      .20347 (mean)
               5.monthsov~a    =    .1167192 (mean)
               6.monthsov~a    =    .1577287 (mean)
               7.monthsov~a    =    .2223975 (mean)
               8.monthsov~a    =    .0268139 (mean)
               9.monthsov~a    =    .0930599 (mean)
               1.base          =    .2507886 (mean)
               2.base          =    .1072555 (mean)
               3.base          =    .1041009 (mean)
               4.base          =    .2634069 (mean)
               5.base          =    .2744479 (mean)
2._at        : numchamber      =    1.184543 (mean)
               reverse         =   -.3420756
               nummask         =    2.055205 (mean)
               ally_relat~s    =   -.0064078 (mean)
               orientatio~s    =    .0139995 (mean)
               infocenter      =    .5567823 (mean)
               orient_meet     =    .5457413 (mean)
               war_interest    =   -.0050959 (mean)
               honolulu_c~t    =   -.0071435 (mean)
               0.age           =    .0078864 (mean)
               1.age           =    .0031546 (mean)
               2.age           =    .0189274 (mean)
               3.age           =    .0694006 (mean)
               4.age           =    .0694006 (mean)
               5.age           =    .1009464 (mean)
               6.age           =    .0804416 (mean)
               7.age           =    .1041009 (mean)
               8.age           =    .0630915 (mean)
               9.age           =    .1876972 (mean)
               10.age          =    .1845426 (mean)
               11.age          =    .1104101 (mean)
               0.rankgrade     =     .011041 (mean)
               1.rankgrade     =    .3974763 (mean)
               2.rankgrade     =    .2839117 (mean)
               3.rankgrade     =    .1876972 (mean)
               4.rankgrade     =    .0820189 (mean)
               5.rankgrade     =    .0378549 (mean)
               0.monthsov~a    =    .0047319 (mean)
               1.monthsov~a    =    .0851735 (mean)
               2.monthsov~a    =     .055205 (mean)
               3.monthsov~a    =    .0347003 (mean)
               4.monthsov~a    =      .20347 (mean)
               5.monthsov~a    =    .1167192 (mean)
               6.monthsov~a    =    .1577287 (mean)
               7.monthsov~a    =    .2223975 (mean)
               8.monthsov~a    =    .0268139 (mean)
               9.monthsov~a    =    .0930599 (mean)
               1.base          =    .2507886 (mean)
               2.base          =    .1072555 (mean)
               3.base          =    .1041009 (mean)
               4.base          =    .2634069 (mean)
               5.base          =    .2744479 (mean)
3._at        : numchamber      =    1.184543 (mean)
               reverse         =    .5774525
               nummask         =    2.055205 (mean)
               ally_relat~s    =   -.0064078 (mean)
               orientatio~s    =    .0139995 (mean)
               infocenter      =    .5567823 (mean)
               orient_meet     =    .5457413 (mean)
               war_interest    =   -.0050959 (mean)
               honolulu_c~t    =   -.0071435 (mean)
               0.age           =    .0078864 (mean)
               1.age           =    .0031546 (mean)
               2.age           =    .0189274 (mean)
               3.age           =    .0694006 (mean)
               4.age           =    .0694006 (mean)
               5.age           =    .1009464 (mean)
               6.age           =    .0804416 (mean)
               7.age           =    .1041009 (mean)
               8.age           =    .0630915 (mean)
               9.age           =    .1876972 (mean)
               10.age          =    .1845426 (mean)
               11.age          =    .1104101 (mean)
               0.rankgrade     =     .011041 (mean)
               1.rankgrade     =    .3974763 (mean)
               2.rankgrade     =    .2839117 (mean)
               3.rankgrade     =    .1876972 (mean)
               4.rankgrade     =    .0820189 (mean)
               5.rankgrade     =    .0378549 (mean)
               0.monthsov~a    =    .0047319 (mean)
               1.monthsov~a    =    .0851735 (mean)
               2.monthsov~a    =     .055205 (mean)
               3.monthsov~a    =    .0347003 (mean)
               4.monthsov~a    =      .20347 (mean)
               5.monthsov~a    =    .1167192 (mean)
               6.monthsov~a    =    .1577287 (mean)
               7.monthsov~a    =    .2223975 (mean)
               8.monthsov~a    =    .0268139 (mean)
               9.monthsov~a    =    .0930599 (mean)
               1.base          =    .2507886 (mean)
               2.base          =    .1072555 (mean)
               3.base          =    .1041009 (mean)
               4.base          =    .2634069 (mean)
               5.base          =    .2744479 (mean)

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
numchamber   |
         _at |
          1  |  -.0229489   .0251002    -0.91   0.412    -.0926383    .0467406
          2  |  -.0254345   .0141648    -1.80   0.147    -.0647624    .0138934
          3  |  -.0295963   .0075492    -3.92   0.017    -.0505562   -.0086364
------------------------------------------------------------------------------
(est1 stored)

. coefplot (est1, msym(O) mfcolor(white) mlcolor(black) msize(large) ciopts(lwidth(.6 1.15) lcolor(black))), vert ci(95 90) ylabel(-.
> 1(.02).06) ymtick(-.1(.01).06) legend(off) ytitle("AME of Gas Chamber Exposure on Support" "for Using Chemical Weapons Against Japa
> n") title(" ") xlabel(1 "25th Percentile" 2 "50th Percentile" 3 "75th Percentile", angle(45)) yline(0, lcolor(cranberry) lpatt(shor
> tdash)) yline(-.1, lcolor(gs10) lpatt(dot)) yline(.06, lcolor(gs10) lpatt(dot)) title("Low News Interest") saving("${result}/chambe
> r_news.gph", replace)
(file ~/Desktop/JOP Replication/Results/chamber_news.gph saved)

. graph export "${result}/chamber_news.png", replace
(file ~/Desktop/JOP Replication/Results/chamber_news.png written in PNG format)

. 
. eststo clear

. 
. graph combine "${result}/chamber_edu.gph" "${result}/chamber_info.gph" "${result}/chamber_news.gph", cols(3)

. graph export "${result}/chamber_hetero.png", replace
(file ~/Desktop/JOP Replication/Results/chamber_hetero.png written in PNG format)

. 
. ********************************************************************************
. 
. clear

. 
end of do-file

. 
. clear

. 
. ********************************************************************************
. 
. clear all

. 
. log off
      name:  <unnamed>
       log:  /Users/christopherblair/Desktop/JOP Replication/jop_blair.log
  log type:  text
 paused on:   3 May 2023, 17:49:50
-------------------------------------------------------------------------------------------------------------------------------------
